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Main idea:
Deepfake is a new form of media manipulation
Argument/fact
-Deepfake can easily reach millions of people through social media.
- Deepfake media is a risk you should be aware of.
-Deepfakes are for humans already very difficult to distinguish from a real photo.
Dialogue:
My opinion of this website is that i didnt know about How to reduce deepfake fraud risk and now i know all of the deepfake fraud risk, i also learned a bit of The risks of deepfake i knew a bit of this but now i know more.
Sumary:
Deepfake is a new form of media manipulation. Deepfake technology uses deep learning , a form of artificial intelligence  to create a fake image, video or audio fragments.

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Deepfake is a new form of media manipulation. Deepfake technology uses deep learning - a form of artificial intelligence (AI) - to create a fake image, video or audio fragments. By using big datasets of images, audio and video it imitates someone's voice or facial expression. This way fraudsters can make (influential) people say or do things they have not really said or done. And beware: it is also already used for financial fraud.

The risks of deepfake

Nowadays, a deepfake can easily reach millions of people through social media. That is why a convincing deepfake can have a negative impact on our society. But also on a smaller scale, deep fake media is a risk you should be aware of.

The risks for financial services
For financial services, deepfake technology can be used to commit fraud in several ways. There are already cases known in which a deepfake cost a company millions of dollars. Fraudsters can for example mimic your CEO or a bank employee to get personal information, make you transfer money or open a bank account to launder money. This way spoofing fraud will be even more convincing.

How to reduce deepfake fraud risk

Deepfakes are for humans already very difficult to distinguish from a real photo, video or audio-file. Therefore the most efficient way to reduce the risk of deepfake fraud is to set up segregation of duties within your financial processes. Make sure your corporate admin sets up the four-eye principle for transactions in InsideBusiness. We can do it for you, if you have no corporate admin set within InsideBusiness.

Find out more

Go to the banking safely page or download the leaflet below.

Safeguard your business against deepfake fraud

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Deepfake: beware of the risk for financial services * ING

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Deepfake is a new form of media manipulation. Deepfake technology uses deep learning - a form of artificial intelligence (AI) - to create a fake image, video or audio fragments. By using big datasets of images, audio and video it imitates someone's voice or facial expression.

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Main idea:
The main Idea are dangers of deepfakes.

Arguments/facts:
-Spread mising information and inspire misunderstunding,fear or disgust.
-Create false narratives of people
-Create pornogrphic content

Dialogue:
I didn't know that 96% of deepfakes are pornographic.

Summary:
Tell us the dangers of deepfakes and why deepfakes have more negative things than possitive and they explain us some examples as Create revange porn and a bunch of more examples. As well they explain us Who is making deepfakes and what is behind technology, as well explain us the implications of deepfakes.
APA:
Avondale Kendja. (October 1st 2021). The dengers of deepfakes. Garbo. Retrived from https://www.garbo.io/blog/deepfakes

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You may have seen these headlines:

Jordan Peele uses AI, President Barack Obama in fake news PSA

Belgian socialist party circulates 'deep fake' Donald Trump video

No, Tom Cruise isn't on TikTok. It's a deepfake

‍

Or, maybe, you've seen the deepfakes of Nicolas Cage, Nancy Pelosi, or Mark Zuckerberg over the last year or two.

But, the reality of deepfake videos, images, and audio is much more dangerous than fake Hollywood celebrity videos.

96% of deepfakes are pornographic and 99% of those mapped faces are sourced from women - and not just women celebrities.

Now let's dive in.

What is a deepfake?

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A deepfake is an image, video, or audio of a person whose face or body is digitally altered so that they look or sound like someone else.

Why is it called a deepfake?

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Deepfakes are a rather new technology.

The word itself is coined from a combination of the term "deep learning" and "fake".

" Deep learning" refers to a particular type of artificial intelligence (AI) - a neural network - and its ability to perform analytical and physical tasks on large data sets without human intervention. In other words, the artificial intelligence is able to learn and process large amounts of data in a short amount of time, adapt to change, and do things without much of a user's effort.

Deep learning is a subset of machine learning, which is itself a subset of AI technology that enables computer systems to learn without users having to direct it closely.

A shallowfake, by contrast, is the product of people using non-deep learning programs to manipulate another's face and/or body. People can use simple video-editing systems, for example, to create less convincing content.

What are deepfakes used for?

‍

"Similar to computer viruses, good deepfakes can be incredibly difficult to detect. But unlike computer viruses, good deepfakes can be created by anyone. For example, any junior high school student can create a deepfake using a 5 year old iPhone," says Ben Colman from Reality Defender.

Often the people who are using deepfake technology use it to:

Spread misinformation and inspire misunderstanding, fear or disgust

Create false narratives of people

Create revenge porn (aka deepfake porn or deepfake nudes - which get ~95,000 searches a month on Google)

Generate a specific public image for the subject (and sometimes make a one of themselves that contrasts and depends on the subject's falsified public image)

Censure or mock the subject

Create pornographic content (often said to be for an individual's sexual pleasure)

Are deepfakes always malicious?

‍

There are some arguments for positive uses of deepfakes including using them for accessibility, education, activism, art, and self-expression. However, the potential and real-life positives of deepfakes does not negate the dominance of deepfakes that regularly harm women and girls.

"With quick search of the iOS or Android AppStore, non-technical users can download applications that make a person appear compromised, or involved in an inappropriate situation or video. We are seeing the growth of deepfakes in revenge porn, and nation state level disinformation campaigns," says Ben.

How to make a deepfake

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Previously, to make a deepfake, a person had to use a facial recognition algorithm and a variational auto-encoder (aka, a VAE, an autoencoder whose encodings distribution is regularised during the training in order to ensure that its latent space has good properties allowing us to generate some new data).

The variational auto-encoder encodes whatever images (aka, training data) the person gives the AI algorithm for the encoder to transform them into new images. The encoder would be "trained" to recognize the original subject's face. The deepfake creator would also need another encoder that's trained in a diversity of faces for contrast.

After training the encoders to recognize, say, Jennifer Aniston's face, and recognize other non-Aniston faces, the deepfake creator will combine the two encoders' information to produce her face on someone else's body.

As for audio deepfakes, it requires enough noise to disguise the otherwise easily identified falseness of the voice.

‍

Now, there are "deepfake makers" and "deepfake apps" and other deepfake software that make creating a deepfake as simple as uploading an image or video file.

‍

Who is making deepfakes?

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Deepfake creators may be political groups, government agencies, social media users, software tech experts, visual effects artists, or the average layman.

While the most credible deepfakes require knowledge of AI systems and design, it's also possible for ordinary people like your friends and family to make a deepfake due to this technology being released publicly via websites anyone can find.

You may have even come across Android and Apple face-swapping apps where users can upload selfies and swap faces for a seemingly innocent moment of fun. There are quite a few of these open-source tools and apps that are seemingly innocent. While it might be fun to perform a face swap, take a second to think about why someone might do this beyond a bit of fun.

‍
Would you doubt or look twice at this image?
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Image Credit: kendja553 @Artbreeder

‍

‍

It's technically a deepfake. Would you have known?

What's the WHY behind the technology

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Some researchers posit that deep learning tech could enable the visually impaired to navigate the world by transferring their navigation skills, using a cane for example, to a virtual world the AI builds. AI with deep learning can also enable educators and teachers to deliver lessons that engage their students through an immersive sensory experience.

"AI-Generated synthetic media can bring historical figures back to life for a more engaging and interactive classroom," suggests Ashish Jaiman, the Director of product management at Microsoft.

However, deep learning is a continuation and progression of artificial intelligence - which inevitably comes with negative consequences.

How do you spot a deepfake?

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Deepfake detection can be both easy and difficult.
Ben agrees - "Anyone can create a deepfake in under 10 seconds, but detecting a deepfake can take days. While multiple detection models exist, they are only as good as the deepfakes used to train them. For example, if a deepfake detection algorithm is not trained to track a person's eye blinking, then it will not recognize deepfakes of people whose eyes have not closed for 10 minutes. For this reason, www.RealityDefender.ai has brought multiple deepfake detection models onto a single platform."
Qualities of a badly made deepfake

bad lip-synching

patchy skin tone

flickering around transposed faces' edges

visible strands on the fringe of hair, where the AI had trouble rendering the image

badly rendered jewelry and teeth

inconsistent lighting and glasses or the iris' reflections

misplaced or misshapen facial features

For

If the subject's voice matches their appearance (is a very high, tinny voice coming from a portly person?)

Whether or not the subject's blinking too much or too little

If their eyebrows really match their face's shape (Are there noticeable shadows? Do they look like extra layers?)

If their hair sits right on their scalp

Whether the skin seems too airbrushed [seemingly like plastic or marble] or over-wrinkled

more convincing deepfakes, the best way for you to spot one is to look out for: include:
"We built Reality Defender in New York City so that we can be close to the top AI researchers across startups, academia, business, and government. This helps us support and influence emerging standards and legislation to accelerate innovations in deepfake detection," says Ben.

Is creating a deepfake a crime?

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The short answer is that it depends on the country - and if in the United States, the state - and how it was used.

On May 2, 2021, South Korean police officers cite creating deepfakes as a crime after making several arrests - mostly of teenagers. One police official stated, "Creation or distribution of deepfake content is a serious crime".

Hogan Lovells, working for a global law firm, reports that many countries in Europe have not included prohibitions against deepfakes.

Within the United States, Texas was the first state to address the legality of deepfakes with the 2019 TX SB751, which directly prohibits and criminalizes deepfake creation, especially in the context of a political election. California also made measures against deepfakes in 2019 by "prohibit[ing] the use of deepfakes in election materials by specifically forbidding the malicious production or distribution of 'materially deceptive' campaign materials within sixty days of an election".

However, these were temporary measures for particular events.

As for an example of a federal law, in 2021, the National Defense Authorization Act (NDAA) recently " ask[ed] for recommendations" that could lay the foundation for federal regulations of such media. The NDAA includes the requirement for National Security officials and officers to create annual reports for the next five years on deepfakes. These reports are supposed to show the potential harm from deepfakes, which may have things from foreign influence campaigns to fraud. Employees at the Pentagon currently work with many of the U.S.'s renowned research institutions to combat the threats deepfakes politically and personally.

The implications of deepfakes

‍
Again, 95% of all deepfakes are pornographic.
Anyone with a good enough program (or access to newly created deepfake creation websites) can transpose and edit another person's face onto someone else. They can even use their voice and make them seem to say something they never said. With how much we depend on the internet for both news and entertainment and how people actively distort reality to create instantly gratifying content, it's sometimes difficult to notice when something is real or if it's something using fantasy to make its own reality.

What does that mean when the wrong hands take advantage of such technology? If it's so easy for people with little to no knowledge of AI systems and deep learning to still use these technologies, does this mean that more people will be inclined to inflict malicious acts against others?

One implication that arises from deepfakes is the circumstance where it becomes "proof" of a person's actions. With deepfakes, an ex-boyfriend can easily put an ex-girlfriend's image on the face of a porn actor to then spread it around in their respective social groups. The victim would face the often negative repercussions of having a taboo action exposed without ever having actually done it.

Women already face disproportionate amounts of discrimination and harassment - often with long-lasting consequences.

Deepfakes can, and often do, ruin lives.

Revenge porn is that much easier to create and claim real to damage someone's reputation.

One woman describes how her Facebook images were transposed into "violently pornographic" images and shared with others who were encouraged to also share them. She highlights how the purpose of these deepfakes is to display the creator's power over the subject in a way saying "This is what I can do to you, and I can do it however I want. You'd better act like how I want you to. There's no room for you otherwise".

Let's be clear - deepfakes are a new form of violence against women.

Finally, deepfakes could absolutely corrode the trust people have not only in the subjects of deepfakes, but of content creators (news outlets, magazines, etc.) and create new forms of online scams.

You can argue that the threat of such identity distortion-making would affect not just how people interact online, but how they're developing their own morals, living standards, ideas of autonomy, and identities.

If anyone can make you look like you enjoy certain activities and things without your consent, do you really have significant control over yourself or your environment?

What would be the point in engaging with the world if you can't safely say or do things without "making" others come after you and potentially ruin your life?

Should you do the same to get at least a modicum of control? (And down a spiral many can go - doing the same that others have or could do against them, and then feeling like they have the right to do the same to them or others.)
"Once a deepfake is shared and re-shared, it is incredibly difficult to remove from circulation. Reality Defender's goals are two-fold:

Stop deepfakes at the source by flagging the offending materials and the offenders themselves

Stop deepfakes at the destination by integrating our solution into social networks, dating platforms, internet browsers and the device themselves"

With the conspiracy-laden, accusatory attitudes and the ease of misinforming and being misinformed, deepfakes are going to have a bigger impact on our lives - both digitally and physically.

While the technology does have potential uses, its disadvantages and dangers outweigh the benefits.

Ben Colman understands the implications of deepfake technology.

Reality Defender's goals are lofty - but needed in today's digital age. While this new technology takes hold, it's up to citizens, and other technology, to combat the dangers it presents.

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Deepfakes are a dangerous new technology enabling gender-based violence in the digital age.

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Main idea:
How deepfakehas b big impact the people in our society.
Arguments/facts:
- Deepfake is a real threat society influencing the sentiments and perceptions of people around us
-are mainly target while creating the Deepfake fake adult videos or images
-few engineers are putting their mind in developing the AI or machine

Dialogue:
I think that is a very interesant article in wich we can see Exploitation of AI and Machine Learning Technology that was a very interesting topic and i didnt know about A Real Threat to Society, Elections and Politicians so it was great.

Summary:
They have deployed engineering team to detect the manipulated videos, photos or videos and also allow users to report such contents their team.

APA

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Deepfake technology is impacting the people and society with devastating consequences likely to be noticed if not controlled timely. Actually, it is kind of serious exploitation of AI and machine learning technology used to perform a face swap to create the illusion that someone either said something that they didn't say or are someone they're not and such changes are not noticeable to human eye compelling people to believe easily.

This unethical practice of using the Deepfake technology has potential consequences in the short time as well a long run and below you can find out what are the consequences for our society and people living there and having threat from such technology. However, using the right Deepfake algorithm such videos or images can be spoted to minimize the impact on people.

Rather using the AI and machine learning technology for betterment and living conditions of the people, few engineers are putting their mind in developing the AI or machine learning enabled fake contents to influence the people in our society. This Face-swapping technology got the greater attention from the unethical people from our society for creating Deepfakes.

Using AI or ML algorithms photos are swap on faces to create fake videos and images giving a negative impact on society about the development and more innovation in the AI and machine learning fields.

People will perceive such technologies in the destructive way that can affect the further or more innovative growth and development in AI and ML fields.
What is Deepfake? Know Everything About this AI-based Technology
Deepfake is a real threat society influencing the sentiments and perceptions of people around us. Popular celebrities, politicians and other personalities is getting badly affected by the misuse of AI-backed such technologies.

Any popular or well-reputed person from our society can be targeted by creating altered pornographic clips or swapped faced with unknown persons like politicians contending in elections can be the victim of Deepfakes.

Such fake videos or images circulating on social media and other online channels can influence the audience at the time of state or central government elections in any country.

1. Basics of Neural Network 2. Are you using the term 'AI' incorrectly? 3. How Can We Improve the Quality of Our Data? 4. Artificial Intelligence Conference

This can play a crucial role in swaying public opinion in the run-up to elections. Preventing such fake news and rumors in the society become impossible especially when the culprits are intentionally and trying to defame the personality of an individual by creating the fake contents.

Another serious consequences of Deepfake is widely used in creating sexually explicit images or videos misrepresenting the celebrities in spoofs. And celebrities like popular actors or stars from the music industry are mainly target while creating the Deepfake fake adult videos or images. The creators swap actresses' faces into videos by adult film stars enabling the transfer of sexual fantasies from imaginations of people to the Internet.

Emma Watson, Natalie Portman and Gal Gadot are the few popular celebrities targeted into Deepfakes. And before experts detect the reality of a deepfake video it goes viral on the internet and influence the large number of people.

However, many adult websites also use deepfakes video detection tools to remove such videos everyday. But the problem is in most of the In most countries, no such laws exits to deal with this kind of content yet, making it difficult to control and badly affecting the image and dignity of well-known personalties.

People around the world keep looking for interesting contents on internet, social media channels are one the most preferred online platforms where such contents are posted without any authentication influencing the group of people on the internet. These social media platforms continued emergence of all forms of manipulated media presents real challenges for society.

One of the leading social media networking sites, FaceBook said, they are actively working to combat the threat of Deepfakes. They have deployed engineering team to detect the manipulated videos, photos or videos and also allow users to report such contents their team.

Social media companies also hire people or Deepfake service providing companies to spot such contents and help them to remove before it can influence more number of people.

The potential consequences from DeepFake technology could be more than your imagination. If it is not controlled timely it's consequences could be more devastating and use of AI-based technologies will get an negative conceptualization among the people around the world. Hence, companies like Cogito offering Deepfake detection services helping online community to spot the fake images, audio or videos created from deepfake technology with best accuracy. This article was originally featured on Visit Here

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How Deepfake Technology Impact the People in Our Society?

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Deepfake technology is impacting the people and society with devastating consequences likely to be noticed if not controlled timely. Actually, it is kind of serious exploitation of AI and machine learning technology used to perform a face swap to create the illusion that someone either said something that they didn't say or are someone they're not and such changes are not noticeable to human eye compelling people to believe easily.

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What Are The Positive Applications of Deepfakes

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What Are The Positive Applications of Deepfakes

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Main idea:
What are deepfakes mainly used for
Arguments/facts
-One of the primary uses of deepfake technologies is pornography
- negative effects of deepfake applications, like scamming and manipulating people,
-there are still many real-life applications that can bring a positive change to the world

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What Are The Positive Applications Of Deepfakes?

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What Are The Positive Applications of Deepfakes are the product of artificial intelligence (AI) applications that merge, combine, replace and superimpose,

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How to detect deepfakes | Deepfakes explained

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Main idea: Deepfakes are bad or good? In deepfakes videos appears a person that is doing and saying thing they never said. Depfakes become better and better, so it is becoming relatively easy and cheap to make high-quality deepfakes.

Arguments/facts:
- Some people used for good things.
- Some people used for bad thing
- Deepfakes are not all bad.

Dialogue: I think deepfakes have good and bad things. As I said in the web page for business deepfakes are very good but other people can use deepfakes for very bad things.

Summary: Like any other technology tool, some people will use this tool for good, and some will use it to do very bad things. Most discussions on this topic highlight the negative aspects of deepfakes, but deepfakes are not all bad. Businesses need to understand the benefits of using deepfakes, especially in marketing.

APA:
Mark Zuckerberg (Sep 11,  2020). Deepfakes Are Bad—Deepfakes Are Good. https://www.forbes.com/sites/glenngow/2020/09/11/deepfakes-are-baddeepfakes-are-good/?sh=4a9a34004ba0

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(This article is part of a series on Artificial Intelligence for Board Members and Senior Executives.)
Most people are familiar with deepfake videos, where it appears that a person is doing and saying things they never did or said. As the AI technology behind deepfakes becomes better and better, so do the results of deepfake creations.

Recent advances in this AI-based technology are happening so quickly that it is becoming relatively easy and cheap to make high-quality deepfakes. Deepfakes are created by AI that continually tests the results of a video, audio, or text until they reach a "realism" threshold. Like any other technology tool, some people will use this tool for good, and some will use it to do very bad things.

Most discussions on this topic highlight the negative aspects of deepfakes. But deepfakes are not all bad. Businesses need to understand the benefits of using deepfakes, especially in marketing.

The dangers of deepfakes are around manipulating an audience to believe something happened that never actually happened.

For example, a deepfake has been used to make it seem like a company's CEO is an evil, nefarious character ( like in this video of Mark Zuckerberg). This video was created well over a year ago, and if it were produced now, you would have a hard time telling this isn't real.
The Benefits of Deepfakes
Many experts fear that deepfakes will be used just before the November election to influence the election. For example, a deepfake can make it seem like a candidate said or did something terrible when it was merely a trick to discredit that candidate. It's even more likely a candidate will do or say something terrible and claim what you see and hear is a deepfake.

Deepfake audio was used to steal $243K from a company when a bad actor used the voice of the CEO to order a subordinate to move money to a supplier.

But all is not doom and gloom. Deepfakes can be very useful to marketers. And because the technology is rapidly improving, we will start to see better and better quality results. Here are some examples

David Beckham, in a commercial about malaria awareness, speaks nine languages.

State Farm has a very creative ad featuring an announcer from many years ago predicting the future correctly. This ad captured the imagination of audiences who turned this into a viral success.

Other areas where deepfake technologies can be good for business include:

Hyper-personalized entertainment featuring the user in the video clip

Avatars that feature your favorite star trying on clothes in a virtual store

Creating advertisements featuring highly-localized spokespeople who may be made from scratch to appeal to locals

The creation of any media when in-person work is discouraged, such as movies, television, streaming media, photoshoots, and more. These media can be created in a studio for much less than it costs to bring in the talent.

Some companies in this sector include Tangent.ai, Tencent, Synthesia, Canny, Modulate AI, and Dessa (acquired by Square).

Most people are concerned about the proliferation of deepfake technology. It's essential to be aware of the dangers. Simultaneously, a whole new marketing world is opening up, enabling a much more significant influence on buyers.

Marketers should be adopting this technology to help improve brand value, increase brand awareness, and move buyers closer to purchasing their products. The businesses that use deepfake technology to engage with their target audiences will gain customer loyalty and market share.

If you care about how AI is determining the winners and losers in business, and how you can leverage AI for the benefit of your organization, I encourage you to stay tuned. I write (almost) exclusively about how senior executives, board members, and other business leaders can use AI effectively. You can read past articles and be notified of new ones by clicking the "follow" button here.

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(This article is part of a series on Artificial Intelligence for Board Members and Senior Executives.) Most people are familiar with deepfake videos, where it appears that a person is doing and saying things they never did or said. As the AI technology behind deepfakes becomes better and better, so do the results of deepfake creations.

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It is surprisingly simple to create a DeepFake, and yet its scary how easy it is to create a thing that could spread rumors world-wide and affect many people

Main idea: How Simple is it to make deepfakes?

Arguments/facts:
- It isn't that easy, There is a big difference between using a model and training a model
-You need an encoder and a decoder to create a deepfake
-Today, almost anyone can manipulate videos, audio, and images to make them look like something else.

Dialogue: I think it is not that simple to create Deepfakes, because there is a lot of complex code you need, and I think you need to be a highly valued coder to create such a thing.

Summary: In conclusion deepfakes can be easy or hard depending on what you are good at, for example a high tech guy could do deepfakes, but someone with no experience in coding is going to fail, this means people like the governments can use this technology to their advantage.

APA
Finger, Lutz. (8th, September 2022). Overview Of How To Create Deepfakes. Forbes. Retrieved from: https://www.forbes.com/sites/lutzfinger/2022/09/08/overview-of-how-to-create-deepfakesits-scarily-simple/?sh=2751fd452bf1

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Deepfakes are simple to make. Scarily simple if you are concerned about abuse. Early deepfakes were - perhaps unsurprisingly - focused on pornography. More terrifying use cases include the use of deepfakes for fake alibis in courtrooms, extortion, or terrorism. In Deepfakes - the danger of Artificial Intelligence that we will learn to manage better, I outline how transparency, regulation, and education will improve the detection of deepfake technology - which could help us combat its misuse. This article will focus more on the technical side of how deepfakes work.

Today, almost anyone can manipulate videos, audio, and images to make them look like something else. You don't need programming skills to create a deepfake. You can create it for free in less than 30 seconds using sites like my Heritage, d-id, or any of the many free deepfake applications. Please use these tools in an ethical and morally acceptable way.

Is it that easy?

Hold on - AI and Deep Learning are that easy? Of course, it's not that easy: There is a big difference between using a model and training a model. Before we can reach the point where we have such a self-serve tool, we must first build a model that enables it. Underlying all deepfake tools are Artificial Intelligence (AI) models. These models need a lot of training data - and creating them is not simple.

These models are based on neural networks. They mimic an architecture inspired by the information processing of nodes in our brains. Unlike our brains, however, artificial neural networks tend to be static and binary, while our brain works dynamically and analog. Looking under the hood of an neural network model, you will see it's a layered stack of regression functions.

AI architecture for deepfakes

An academic study by Goodfellow et al. in 2014 reinvigorated the interest in deepfakes through a new deep learning architecture called Generative Adversarial Networks (GANs). GAN set up two neural networks to compete against each other (hence the "adversarial"). The first neural network is a so-called generative neural network. Based on a random seed, it creates a realistic image through a process called decoding. More on this is below. It works like a reverse pixelate effect on images:

Since the initial input is random, the created image is entirely fake. Check out, for example, the images at thispersondoesnotexist.com. All of them are fake. All of them were generated based on a random seed.

Now you're thinking: What about the other neural network set up by GAN? I'm glad you ask - this is where the "adversarial" component comes in. The second neural network - called the discriminative classifier - checks the first, generative neural network. Essentially the second neural network is checking whether the image of the first network is real or fake. This way, the two neural networks train each other and become more and more realistic.

This is pretty cool! Today, though, most tools for deepfakes are not using GANs. Rather, they leverage either encoder-decoder pairs or first-order motion models. Let's explain what both of those are.

Encoder-decoder pairs

Deep Learning models have different layers. Each layer represents a mathematical abstraction of the prior layer. We call this a latent representation. Or said differently, what you see in those layers (if you were to open up the Deep Neural Network Black Box) is no longer the reality that one can observe, but rather an inferred state based on the mathematical model of the AI layers before. Going from an original image to a latent image is called encoding. The process is similar to humans. We see a cat, and we use the word (e.g., representation) "CAT" for it. How do we know that a given object is a cat? Well, we have seen many cats. Thus we identify them. Our brain has created a connection between the image and the encoded-word "cat."

Let's turn this process around. Close your eyes and picture a cat. Can you? Sure you can. How? By using the knowledge stored in your brain. Computers do this the same way. Once the computer has encoded many similar images (like images from a cat etc.), it can reverse this process, and go from "cat" to an image. This process is called decoding. Take a look at Dall-E 2 to see how powerful this process can be.

Below is the process of encoding and decoding an image of a "2". After the encoding stage, the computer stores the information or latent image for a number this is just the information "this is a two". Next, the decoder will revert this information back into an image of a "2 "based on how the computer imagines it.

To create a Deepfake Generator, we need such an Encoder-Decoder pair. The encoder extracts latent features of face images, and the decoder uses this information to reconstruct the face images from the latent features. The encoder's job is to draw a latent version of the original image that captures their emotions/expressions. The decoder's job is then to "re-draw" the original image from the latent version. The encoder and the decoder are recurrent neural networks that train themselves to improve exponentially by practicing on thousands of source/target images.

To generate a deepfake, the decoder for the target draws the target image with the source's latent features (expressions), and voila! We have a deepfake image. The image below is from Nguyen et al. paper and shows the process.

First-Order Motion Model

A slightly different approach for deepfakes is to replace the encoder with a motion model. Anyone who has used Snap will have used such an approach. The underlying AI detects facial expressions, eye movements, and head position - which are then superimposed on either a unicorn, a potato, or whatever Snap-Filter is en-vogue.

The initial neural network is trained on many hours of real video footage of people to help the AI recognize various important features of the person's face - such as the eyes, upper and lower lips, teeth, ears, eyebrows, an outline of the face, etc. The computer automatically maps the right parts of one's source images onto the destination image. Since a video is a collection of images (frames), placed one after the other, this neural network allows us to photoshop each frame in a video.

To explain how deepfakes work, we created a simple Google co-lab for you that builds on the initial code from Aliaksandr Siarohin - who (no surprise here) - works for Snap. In our Google co-lab, all you need to do is upload a folder with the target image, upload your own video (source) to your google drive, and run the co-lab notebook block-by-block. With a bit of post-processing, you can make your own short deepfake video. For example, I use this little video to welcome my students to class each term.

Do you want to learn more about how it works? Aliaksandr posted a very good video explaining that here.

It's just the beginning

Tools to create deepfakes are constantly improving. As you see below, the amount of research done in this space is also rising exponentially. There's more to come, and we will be here to update you.

MORE FROM FORBES Deepfakes - The Danger Of Artificial Intelligence That We Will Learn To Manage Better

This article was written with Prithvi Sriram, who has not only been a student of the course but helped create tool sets that future students can use to get their hands on Deep Learning. He currently works at Infinitus Systems, a late series B healthcare startup, where he was the founding member of the analytics team.

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Overview Of How To Create Deepfakes - It's Scarily Simple

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Deepfakes are simple to make. Scarily simple if you are concerned about abuse. Early deepfakes were - perhaps unsurprisingly - focused on pornography. More terrifying use cases include the use of deepfakes for fake alibis in courtrooms, extortion, or terrorism.

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Main idea:
How to spot a Deepfake

Arguments/facts:
-Deepfakes aren't good at recreating side views
-There are many methods to detect deepfakes
-Deepfakes this really can be used for identity theft

Dialogue: I think Deepfakes being this real could be a threat to many people, especially high-valued people such as the president and other high-class people, this could affect many laws.

Summary: They are surprisingly easy to catch, there are many methods, such as the way they cough or sneeze. Deepfakes aren't very good at rendering or recreating side views, there are many reasons for this but that's its weak point.

APA:
Tung, Liam (9th, August 2022). How to spot a deepfake? One simple trick is all you need. ZDNET. Retrieved from https://www.zdnet.com/article/how-to-spot-a-deepfake-one-simple-trick-is-all-you-need/

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Image: Getty Images/Prostock-Studio

With criminals beginning to use deepfake video technology to spoof an identity in live online job interviews, security researchers have highlighted one simple way to spot a deepfake: just ask the person to turn their face sideways on.

The reason for this as a potential handy authentication check is that deepfake AI models, while good at recreating front-on views of a person's face, aren't good at doing side-on or profile views like the ones you might see in a mug shot.

Metaphysics.ai highlights the instability of recreating full 90° profile views in live deepfake videos, making the side profile check a simple and effective authentication procedure for companies conducting video-based online job interviews.

Deepfakes or synthetic AI-enabled recreations of audio, image and video content of humans has been on the radar as a potential identity threat for several years.

However, in June, the Federal Bureau of Investigations warned it had seen an uptick in scammers using deepfake audio and video when participating in online job interviews, which became more widely used in the pandemic. The FBI noted that tech vacancies were targeted by deepfake candidates because the roles would give the attacker access to corporate IT databases, private customer data, and proprietary information.

The FBI warned that video participants could spot a deepfake when coughing, sneezing or other sounds don't line up with what's in the video. The side profile check could be a quick and easy-to-follow way for humans to check before beginning an online video meeting.

Writing for Metaphsyics.ai, Martin Anderson details the company's experiments. Most of deepfakes it created failed obviously when the head reached 90° and revealed elements of the person's actual side profile. The profile view recreation fails because of a lack of good-quality training data about the profile, requiring the deepfake model to invent or "inpaint" a lot about what's missing.

Part of the problem is that deepfake software needs to detect landmarks on a person's face to recreate a face. When turned side on, the algorithms only have half the landmarks available for detection compared to the front-on view.

Anderson notes major weaknesses in using side profile video to recreate a face are the limits of 2D-based facial alignment algorithms and a plain lack of profile data for most people except Hollywood stars.

Arguing the case for using the side profile for authentication in live video meetings, Anderson points out there will likely be a persistent shortage of side view training data for average people. There's little demand for stock photos of profile head shots because they're not flattering, and no motivation for photographers to supply them either since they offer little emotional insight into a face.

"That paucity of available data makes it difficult to obtain a range of profile images on non-celebrities that's diverse and extensive enough to train a deepfake model to reproduce profile views convincingly," writes Anderson.

"This weakness in deepfakes offers a potential way of uncovering 'simulated' correspondents in live video calls, recently classified as an emergent risk by the FBI: if you suspect that the person you're talking to might be a 'deepfake clone', you could ask them to turn sideways for more than a second or two, and see if you're still convinced by their appearance."

Sensity, a maker of liveness detection and deepfake detection software, in May reported that it found nine out of the 10 widely adopted biometric verification systems used in financial services for Know Your Customer (KYC) compliance were severely vulnerable to deepfake 'face swap' attacks.

Commonly used liveness tests that involve a person looking into a camera on a connected device were also easily duped by deepfakes, it found. Liveness tests require the person to move their head left and right and smile. The deepfakes Sensity used involved the would-be fraudster moving their head left and right, but the video shows they stop turning their head before it reaches 90°.

Sensity's CEO and Chief Scientist Giorgio Patrini confirmed to Metaphysic.ai they did not use full 90° profile views in their tests.

"Lateral views of people faces, when used as a form of identity verification, may indeed provide some additional protection against deepfakes. As pointed out, the lack of widely available profile view data make the training of deepfake detector very challenging."

Another useful way to rattle a live deepfake model is to ask the video participant to wave their hands in front of their face. It disrupts the model and reveals latency and quality issues with the superimposition over the deepfake face.

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How to spot a deepfake? One simple trick is all you need

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With criminals beginning to use deepfake video technology to spoof an identity in live online job interviews, security researchers have highlighted one simple way to spot a deepfake: just ask the person to turn their face sideways on.

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The good, the bad, and the future of deepfakes

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The good, the bad, and the future of deepfakes

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Main idea:
Despite an increasing number of positive applications, the dangers of deepfakes continue to elicit widespread concern as they become more widely known and understood.

Arguments/facts:
- What is deepfake?
- How they are created?
- The good and optimistic view.

Dialogue: I think that this web page tells very good in general deepfakes. What is deepfakes, how is created and a good optimistic view.

Summary:
Deepfake combines the word deep (from learning) and fake. Deepfake are enabled by deep learning technology.
To create a deepfake video of an existing person, a neural network traditionally needs to be trained with lots of video footage of this person. While the less-than-kosher uses of deepfakes are frightening to imagine, there are also many advantages to this technology.

APA:
no name ( April 23, 2021). The good, the bad, and the future of deepfakes. https://blog.richardvanhooijdonk.com/en/the-good-the-bad-and-the-future-of-deepfakes/

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What exactly are deepfakes again? A refresher

How are deepfakes created?

The good - an optimistic view

The dangers of deepfakes

What can we do to tell fake from real?

The future of fake and other considerations

Despite an increasing number of positive applications, the dangers of deepfakes continue to elicit widespread concern as they become more widely known and understood. We're bombarded with content detailing the incredible speed at which this deep learning technology is being developed, how deepfakes are becoming increasingly sophisticated and more easily accessible, and what the risks are when this tech falls into the wrong hands. Whether we like it or not, and no matter how troubling the negative implications of the use of deepfakes may be, they're not going anywhere. They're here to stay. But aside from the negative press deepfakes have been getting, there seem to be many reasons to be excited about this technology and its many positive applications as well. For instance, deepfake tech will enable entirely new types of content and democratise access to creation tools that have, until now, been either too expensive or too complicated to use for the average person. On the whole, the ability to use artificial intelligence to create realistic simulations might even be a positive thing for humanity.

What exactly are deepfakes again? A refresher

It's not easy to agree on a comprehensive definition of deepfakes, but let's give it a try. The term deepfake combines the words deep (from deep learning) and fake. We know that deepfakes are enabled by deep learning technology, a machine learning technique that teaches computers to learn by example. Deepfake technology uses someone's behaviour - like your voice, imagery or typical facial expressions, or body movements - to create entirely new content that is virtually indistinguishable from authentic content. This technology can also be used to make real people appear to say or do things that they never said or did, or to replace anyone in an existing video, or create video content with either completely non-existent characters, celebrities, or important political figures. The manipulation of existing, or the fabrication of new digital imagery is not new - in fact, AI-generated pornographic content first surfaced in late 2017. And while creating these types of effects used to take experts in high-tech studios at least a year to accomplish, the rapid development of deepfake technology in recent years has made the creation of really convincing fake content a lot easier and faster. Initially used to describe specific pornographic content, the term deepfakes is now being used more broadly and can refer to a host of different AI-generated, or synthetic, video content.

How are deepfakes created?

To create a realistic deepfake video of an existing person, a neural network traditionally needs to be trained with lots of video footage of this person - including a wide range of facial expressions, under all kinds of different lighting, and from every conceivable angle - so that the artificial intelligence gets an in-depth 'understanding' of the essence of the person in question. The trained network will then be combined with techniques like advanced computer graphics in order to superimpose a fabricated version of this person onto the one in the video. Although this process is a lot faster than it was only a couple of years ago, getting really credible results is still quite time consuming and complicated. The very latest technology, however, such as Samsung AI technology that has been created in a Russian AI lab, makes it possible to create deepfake videos using only a handful of images, or even just one.
Some notorious deepfakes that recently made the headlines: A fake video of Mark Zuckerberg, created by two artists, in collaboration with advertising company Canny, showed up on Instagram. In the deepfake, Zuckerberg gives a rather sinister speech about the power of Facebook. Another fake video shows former President Barack Obama calling President Donald Trump a "total and complete dipshit". Halfway through the video, it becomes clear that Obama actually never said these words - they were actually from writer and 'Get Out' director Jordan Peele. And by far one of the most realistic deepfakes around at the moment are these very recent ones that showed up on TikTok, in which you can see Tom Cruise (or is it?) sharing videos of himself playing golf and goofing around.
The good - an optimistic view

While the less-than-kosher uses of deepfakes are frightening to imagine, there are also many advantages to this technology, and new, good uses for deepfakes are found on a regular basis. Think, for instance, of editing video footage without the need for doing reshoots, or recreating artists that are no longer with us to perform their magic, live. Researchers at Samsung's AI lab in Moscow, for instance, recently managed to transform Da Vinci's Mona Lisa into video. They used deep learning to enable this famous lady to move her head, mouth, and eyes. Deepfake tech was also used at the Dalí Museum in Florida to display a life-sized deepfake of the surrealist artist delivering a variety of quotes that Salvador Dalí had written or spoken during his art career. Deepfake technology will enable us to experience things that have never existed, or to envision a myriad of future possibilities. Aside from many different potential applications in arts and entertainment, think of all the magic this tech could do in education and healthcare. Stay tuned for some more interesting examples of this groundbreaking technology.
Voice manipulator produces speech from text
Adobe's VoCo software - which is still in its research and prototyping stages - enables you to produce speech from text and edit it just like you would images in Photoshop. So, let's assume you would like to have your film clip narrated by, say, David Attenborough or Morgan Freeman; VoCo will make this possible without paying top dollar for actual top narrators doing the job. How the software works is that it takes an audio recording and alters it by including words and phrases that the original narrator never actually said. At a live demo in San Diego, an Adobe operator took a digitised recording of a man saying "I kissed my dogs and my wife" and changed it to "I kissed Jordan three times". All it took to get to this result was to start off with a 20-minute voice recording, alter the transcribed version of the text and then press a button - creating the altered voice clip. As impressive as this may be, developments like this could further fuel the already problematic fake news issues and undermine the public's trust in journalism. Adobe, however, has said it's taking action to address these potential challenges.
Convincing dubbing through automated facial re-animation
Synthesia, an AI software company founded by a team of researchers and entrepreneurs from Stanford, University College London, Cambridge, and Technical University of Munich, is pioneering a new kind of media - facial re-animation software - for automated and very convincing dubbing. The AI startup was put on the map with the introduction of a synthetic video featuring David Beckham talking about raising awareness about the deadly disease Malaria in nine different languages. This technology can be used in various ways and can expand the reach of creators around the world. Synthesia and international multimedia news provider Reuters recently collaborated to create the world's first synthesised, presenter-led news reports. To do this, they used basic deepfake technology to create new video reports from pre-recorded clips of a news presenter. By far the most remarkable is that this technology enables the automatic generation of video reports that are personalised for individual viewers. In learning and development, Synthesia's technology can be used to create video modules in over 40 languages and to easily update and create new content. For corporate communications, the tech can help turn text into video presentations within minutes and easily turn slides into videos without the need for video editing skills.
Deepfakes can make anyone dance like a pro
Tinghui Zhou, co-founder and CEO of Humen AI, a startup that creates deepfakes for dancing, and his research colleagues at UC Berkeley, use a type of artificial intelligence called generative adversarial networks (GANs) to read someone's dance moves and copy them on to a target body. With this technology, you can make anyone dance like, for instance, Bruno Mars. The system can be used for all kinds of dance styles - such as ballet, jazz, modern, or hip hop. First, videos of the source dancer and the target dancer are recorded. Then, the dancers are transformed into stick figures. After that, the swap happens, with a neural network synthesising video of the target dancer, based on the stick figure movements of the source dancer - and, voilà! All it takes is a bit of video footage and the right AI software. It's impressive work, and this type of video manipulation would traditionally take an entire team a couple of days to do. Humen AI aims to turn the dance video gimmick into an app and, eventually, a paid service for ad agencies, video game developers, and even Hollywood studios. Ricky Wong, cofounder of Humen AI, says: "With three minutes of footage of movement and material from professionals, we can make anyone dance. We're trying to bring delight and fun to people's lives," while Zhou adds: "The future we are imagining is one where everyone can create Hollywood-level content."
Smart assistants and virtual humans
Smart assistants like Siri, Alexa, and Cortana have been around for a while, and have significantly improved in recent years. They do, however, still feel more like a new user interface that you need to give exact instructions to, instead of a virtual being you can interact with in a more natural way. And one of the most important steps toward creating credible virtual 'human' assistants that we can interact with is the ability to mimic faces, expressions, and voices. These so-called virtual humans are slowly but surely entering the mainstream in the form of digital influencers that we interact with the way we interact with other people. And while digital influencers don't really respond to you in their own words as their content is created by storytellers, they are heralding a future of 'natural' interaction with true virtual beings. So, deepfake tech that is trained with tons of examples of human behaviour could give smart assistants the capacity to understand and produce conversation with lots of sophistication. And thanks to this same technology, even digital influencers could develop the ability to visually react - in real time - in credible ways. Welcome to the future of virtual humans.
Deep generative models offer new possibilities in healthcare
Deepfake technology can also have many benefits in other areas, such as healthcare. The tech can be used to synthesise realistic data to help researchers develop new ways of treating diseases without being dependent on actual patient data. Work in this area has already been carried out by a team of researchers from the Mayo Clinic, the MGH & BWH Centre for Clinical Data Science, and NVIDIA, who collaborated on using GANs (generative adversarial networks) to create synthetic brain MRI scans. The team trained their GAN on data from two datasets of brain MRIs - one containing approximately two hundred brain MRIs showing tumours, the other containing thousands of brain MRIs with Alzheimer's disease. According to the researchers, algorithms trained with a combination of 'fake' medical images and 10 per cent real images became just as proficient at discovering tumours as algorithms trained only with real images. In their paper the researchers say: "Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. We propose a method to generate synthetic abnormal MRI images with brain tumours by training a generative adversarial network. This offers an automatable, low-cost source of diverse data that can be used to supplement the training set." As the images are synthetically generated, there are no privacy or patient data concerns. The generated data can easily be shared between medical institutions, creating an endless variety of combinations that can be used to improve and accelerate their work. The team hopes that the model will help scientists generate new data that can be used to detect abnormalities quicker and more accurately.
The dangers of deepfakes
As exciting and promising as deepfake technology is, there are very real threats as well. One of the most significant ones being non-consensual pornography, which accounts for a terrifying 96 per cent of deepfakes currently found online, according to a DeepTrace report. There have also been several reports of deepfake audio being used for identity fraud and extortion. The use of deepfakes has the potential to be a huge security and political destabilisation risk, as the technology can be used to spread fake news and lead to an increase in fake scandals, cybercrime, revenge porn, harassment, and abuse. There's also a pretty good chance that video and audio footage will soon become inadmissible as evidence in court cases as deepfakes are becoming increasingly indistinguishable from authentic content. According to the Brookings Institution, the social and political dangers posed by deepfakes include "distorting democratic discourse; manipulating elections; eroding trust in institutions; weakening journalism; exacerbating social divisions; undermining public safety; and inflicting hard-to-repair damage on the reputation of prominent individuals, including elected officials and candidates for office." Deepfakes can cause serious financial problems, too. Some examples include a UK energy firm that was duped into a fraudulent $243 million transfer, and an audio deepfake that was used to scam an American CEO out of $10 million. And we have some more examples for you, so read on.
New Year's video address provokes attempted military coup
The fact that more and more - and increasingly sophisticated - deepfakes are circulating on the internet will progressively mean that any video that seems slightly 'off' can potentially cause chaos. One example is Gabon's president Ali Bongo's New Year's video address in 2019. As the president had been absent from the public eye for some time, and the government's lack of answers was only fuelling doubts, the video caused people in Gabon, as well as international observers, to grow increasingly suspicious about the president's well-being. The aim of the video was to eliminate any speculations, but this plan backfired, as Bongo's critics weren't convinced of its authenticity. A week after the release of the video, Gabon's military attempted an ultimately unsuccessful coup, citing the awkwardness of the video as proof that something wasn't right with the president. Hany Farid, a computer science professor who specialises in digital forensics, says: "I just watched several other videos of President Bongo and they don't resemble the speech patterns in this video, and even his appearance doesn't look the same." Farid added, while noting that he was unable to make a definitive assessment, that "something doesn't look right." The fear of deepfakes' potential to sow this type of discord has caused the US and China to discuss new legislation and the Pentagon's DARPA to invest in technology to detect fake content.
Deepfakes used to blackmail cheerleaders
Images taken from social media accounts have recently been used by a mother from Pennsylvania in the US to create incriminating deepfakes to get her daughter's cheerleading rivals expelled, so that her daughter's team, who competed against other high school teams, could win a competition. The woman had manipulated the images of her daughter's opponents to show them in compromising positions, drinking, smoking, and even naked. The woman also sent the girls disturbing messages in which she urged them to kill themselves. The police have not taken action against the daughter, as there is no evidence that she was aware of what her mother was doing. The mother, however, is facing misdemeanour allegations of cyber abuse and related offenses. Bucks County DA Matt Weintraub says of the first victim: "The suspect is alleged to have taken a real picture and edited it through some photoshopping app to make it look like this teenaged girl had no clothes on to appear nude. When in reality that picture was a screengrab from the teenager's social media in which she had a bathing suit on."
Deepfake bots on Telegram create nudes of women and children
Last year, more than 100,000 deepfake images depicting fake nudes were generated by an ecosystem of bots, requested by Telegram users. The focal point of this ecosystem is an AI-powered bot that enables users to 'strip' the clothes off images of women, so that they appear naked. According to a report by visual threat intelligence company Sensity, "most of the original images appeared to have been taken from social media pages or directly from private communication, with the individuals likely unaware that they had been targeted. While the majority of these targets were private individuals, we additionally identified a significant number of social media influencers, gaming streamers, and high profile celebrities in the entertainment industry. A limited number of images also appeared to feature underage targets, suggesting that some users were primarily using the bot to generate and share pedophilic content." The deepfakes have been shared across various social media platforms for the purpose of public shaming, revenge, or extortion. Most deepfake bots make use of DeepNude technology, but similar apps have started popping up across the internet. All you have to do is upload a photo, and a manipulated image is returned within minutes. Unfortunately, as Telegram makes use of encrypted messages, it's easy for users to create anonymous accounts that are virtually impossible to trace. And while the objective of encryption technology is to protect privacy and assist people in evading surveillance, it's easy to see how these features can be abused.

What can we do to tell fake from real?

As it stands, the number of deepfake videos circulating online has been increasing at an astounding estimated annual rate of 900 per cent. As technological advances have made it increasingly simple to produce deepfake content, questions are being raised about ways to prevent the malicious use of this technology. One way, just like in the case of cybercrime and phishing, is to raise public awareness and educate people about the dangers of deepfakes. Many companies have launched technologies that can be used to either spot fake content, prevent its spread, or authenticate real content using blockchain or watermarks. The downsides are, however, that these detectors or authenticators can also immediately be 'gamed' by those same malicious actors to create even more convincing deepfakes. We've gathered some examples of technologies that have been developed to combat the misuse of deepfakes.
Social media deepfake policies
Social media networks play the most important role in ensuring that deepfakes aren't used for malicious purposes. Basically, their policies currently treat deepfakes like any other content that is misleading or could lead to people getting hurt. For instance, Instagram's and Facebook's policy is to remove 'manipulated media' - with the exception of parodies. YouTube's policy on 'deceptive practices' prohibits doctored content that's misleading or may pose serious risks, and TikTok removes 'digital forgeries' - including inaccurate health information - that are misleading and cause harm. Reddit removes media that impersonates individuals or entities in a misleading or deceptive manner, but has also created an exemption for satire and parody. With the volume and sophistication of deepfakes skyrocketing, it's unclear, however, how social networks will be able to keep up with enforcing these policies. One thing they could do is automatically label deepfakes, irrespective of whether or not the deepfakes cause harm, so that they can at least create more awareness.
Spotting super-realistic deepfake images
Researchers at the University of Buffalo have come up with an ingenious new tool for spotting super-realistic deepfakes. In their paper, the researchers explain how they created a method for distinguishing authentic images from those generated by deepfake technology, and they basically do this by looking closely at the eyes of the person in the image. What the researchers found is that the reflections in both eyes of the person in an authentic photograph are usually identical as a result of the same light environment. This is, however, mostly not the case when it comes to doctored images. Their technology, when tested on deepfake-generated images, managed to pick out deepfakes in 94 per cent of cases. The tech is most accurate, however, when used on photos taken in the portrait setting, which is generally the preferred option when shooting close up portrait photographs.
Genuine presence assurance
Being able to verify that the person you see in online content is authentic is critical in the fight against the misuse of deepfakes, and iProov Genuine Presence Assurance can do exactly that. iProov's system uses biometric scans that can help identify whether the person in question is indeed a live human being and not a photo, video, mask, deepfake, or other replay or presentation attempting to spoof a (biometric) security system. The system works on mobile devices, computers, or at unattended kiosks, and is used by organisations around the world, such as the UK's NHS. The health service has chosen iProov's biometric facial authentication to improve users' remote onboarding experience. Thanks to iProov's Flashmark facial verification technology, users can log in to the NHS app remotely and securely to make appointments, access medical records, and order prescriptions. The process involves submitting an ID photo and positioning one's face on the screen. After a short sequence of flashing colours, the user's identity is verified and the NHS app can be accessed.
Antivirus for deepfakes
Sensity, an Amsterdam-based company that develops deep learning technologies for monitoring and detecting deepfakes, has developed a visual threat intelligence platform that uses the same deep learning processes used to create deepfakes, combining deepfake detection with advanced video forensics and monitoring capabilities. The platform - a kind of antivirus for deepfakes, if you will - monitors over 500 sources across the open and dark web, where the likelihood of finding malicious deepfakes is high. It then alerts users when they're watching something that's potentially AI-generated synthetic media, providing detailed severity assessments and threat analyses. Sensity also enables you to either submit URLs or upload your own image and video files. It then analyses them to detect the latest AI-based media manipulation and synthesis techniques, including fake human faces in social media profiles, dating apps, or financial services accounts online. Sensity also offers access to the world's most comprehensive deepfake database and other visual media targeting public figures, including insights on most targeted industries and countries.

The future of fake and other considerations

Pandora's box has opened, and it looks like the competition between the creation and detection and prevention of deepfakes will become increasingly fierce in the future, with deepfake technology not only becoming easier to access, but deepfake content easier to create and progressively harder to distinguish from real. Deepfakes will continue to evolve and spread further, and issues like the lack of details in the synthesis will be overcome, and with lighter-weight neural network structures and advances in hardware, training and generating time will be significantly reduced. We've already seen new algorithms that can deliver increasingly higher levels of realism and run in near real time. According to experts, GANs (generative adversarial networks) will be the main drivers of deepfakes development in the future, and these will be near-impossible to distinguish from authentic content.

And while the use of deepfakes for good is increasing rapidly in sectors like entertainment, news, and education, these developments will, conversely, also introduce even more serious threats to the public, including increasing criminal activity, the spread of misinformation, synthetic identity fraud, election interference, and political tension. Another aspect to consider is that deepfakes mess around with agency and identity. You can make someone do something that they've never done, using someone's photo - for which the owner has probably never given his or her permission. And once a video is online, you lose all control over how it is used or interpreted.

Going forward, in order to minimise deception and curb the undermining of trust, technical experts, journalists, and policymakers will play a critical role in speaking out and educating the public about the capabilities and dangers of synthetic media. And if we, the public, can educate ourselves to only trust content from reputable sources, we might find that the good uses of deepfakes could actually outweigh the bad. With increased public awareness, we could learn to limit the negative impact of deepfakes, find ways to co-exist with them, and even benefit from them in the future.

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The good, the bad, and the future of deepfakes

# data[].attributes.attachment_link.description
What exactly are deepfakes again? A refresher How are deepfakes created? The good - an optimistic view The dangers of deepfakes What can we do to tell fake from real? The future of fake and other considerations Despite an increasing number of positive applications, the dangers of deepfakes continue to elicit widespread concern as they become more widely known and understood.

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Deepfake Technology Pros & Cons For Digital Marketing

# data[].attributes.subject
Deepfake Technology Pros & Cons For Digital Marketing

# data[].attributes.body
Main idea:
Is the impact of Deepfake technology that is changing the way of people see digital marketing.

Arguments/facts:
- Marketers save money using deepfake.
- It saves time for businesses.
- Marketers can easily repurpose content.

Dialogue: I think this is much better for everything because you don't need actors in-person, you don't need to waste so much money and time. In summary is much better.

Summary:
Marketers save money using deepfakes on video campaigns because in-person actors are unnecessary.
This process saves time for businesses that want to use their employees for ads.
Since deepfakes don't need in-person actors, marketers can easily repurpose content for numerous marketing channels with less time and money.

APA:
No name. (No date). Deepfake Technology Pros & Cons For Digital Marketing. https://www.searchenginejournal.com/deepfake-technology-digital-marketing/454395/#close

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Deepfake technology is changing the way people see digital marketing.

Though marketers are still in the early stages of experimenting with deepfakes and deepfake technology, these videos convey a more i mmersive marketing experience through storytelling.

Deepfake technology is a type of "deep learning."

Deep learning is a machine learning type that allows computers to learn tasks independently without being explicitly programmed.

Deepfake technology also involves computer vision, allowing computers to recognize objects in images.

For example, computer vision uses deep learning algorithms to identify objects in photos or videos. So it can tell you whether there's a dog in your photo or not!

In addition to computer vision and deep learning technologies, the creation process for deepfakes involves image synthesis:

Taking one image (like someone holding up an American flag).

Combining it with another image (a person holding up an Australian flag).

And creating something new from these two components (a person holding up both flags).

Deepfake Examples

If the concept of deepfakes is still hard to grasp, here are examples of deepfake videos that have circled the web:

Deepfake Pros

This type of technology is beneficial for marketers in three ways:

First, it can lower the cost of video campaigns.

Second, it can create better omnichannel campaigns.

Third, it can provide a hyper-personalized experience for the customer.

Low Cost

Marketers can save money on video campaigns using deepfakes because in-person actors are unnecessary.

Instead, they can purchase a license for an actor's identity, use previous digital recordings, insert the appropriate dialogue and create a new video.

This process also saves time for businesses that want to use their employees for ads.

So, for example, if a CEO doesn't have time to record a new ad, marketers only need a few previous recordings to create a new campaign.

Additionally, when creating a deepfake, there's no need to reshoot the footage.

This is particularly useful for marketers on a tight budget who still want to produce high-quality content for their campaigns.

Better Omni Channel Campaigns

Since deepfakes don't need in-person actors, marketers can easily repurpose content for numerous marketing channels with less time and money.

Instead of reshooting the campaign to accommodate different media, marketers can edit the video cuts to create a social campaign.

Or, they can create new synthetic dialogue to create a podcast or radio ad.

Hyper-Personalization

This technology has led to an increase in hyper-personalization.

Brands can provide more relevant messaging and experiences for individual customers based on their personal preferences - such as the color of their skin.

Say a customer was a different ethnicity than a brand's model for marketing.

Deepfake technology can change the skin tone of that model so the customer can experience what the product would look like on their skin tone.

This process helps brands increase inclusivity and reach a broader market.

Also, if a video is needed in multiple languages, deepfake technology can help.

Marketing messages can be personalized by location at the push of a button.

Deepfake Cons

Unfortunately, deepfake videos have been used for more nefarious purposes.

For marketers, this can mean fake customer complaints, fake product reviews, and an overall decrease in customer trust.

Lack Of Trust

The most apparent impact of deepfakes is that they are used to create fake videos, which means that verifying the authenticity of any given video becomes more challenging.

Even if you could tell for sure whether or not someone's image was actual or not before viewing a video, it is still impossible for anyone who didn't know the person personally.

For marketers, using deepfake videos could violate ethics if consumers feel manipulated by the campaign.

For example, if marketers use deepfakes to create fake positive reviews, that practice would be considered unethical.

On the other hand, if marketers use deepfakes to further a brand's storytelling, that could be regarded as ethical.

Increase In Scams

Deepfake technology could increase potential scams, such as creating false accusations against companies.

These videos are made by taking recordings of actual incidents and changing the audio with new dialogue to make it seem like something that it isn't.

For example, a German energy firm's U.K. subsidiary handed over nearly $250,000 to a Hungarian bank account after a scammer used deepfake technology to mimic the CEO's voice.

In addition, manufacturers can use this type of deepfake technology to create fake customer testimonials or product reviews that make their products appear more appealing than they are.

How Marketers Can Use Deepfakes In Their Campaigns

Despite the unfortunate ways people use deepfakes, marketers can make their campaigns come to life quickly with this technology.

Influencer Campaigns

Imagine booking one of the top influencers for a campaign.

You only need a bank of digital footage instead of requiring them to shoot a video for hours.

Artificial intelligence and machine learning do the rest.

Or, you could use historical influencers, such as Marilyn Monroe or Audrey Hepburn.

Since there are a plethora of video and voice recordings of them, marketers can use their likeness in a deepfake to boost their campaign.

Experiential Campaigns

To stand out against the crowd, brands can use deepfakes to immerse the consumer into a shopping experience.

For example, ecommerce stores can superimpose a shopper's face onto a model's body to see how the clothes look.

Nostalgic Ad Campaigns

State Farm created one famous example of deepfake technology.

The insurance company created an ad for the series The Last Dance by superimposing 1998 Sportscenter footage to make it look like Kenny Mayne predicted the documentary.

This deepfake was made purely for entertainment and created nostalgia with viewers that remember that iconic time for the Chicago Bulls basketball team.

Product Demos

Product demos could become experiential for customers.

Instead of using the same b-roll for all clients, marketers could create personalized demos that show the actual client using their product. It can't get more personal than that, can it?

This technology is here to stay, and it will continue to evolve.

In the digital marketing space, deepfake technology has both advantages and disadvantages.

While there are ethical implications, deepfake videos allow brands to stretch their marketing budgets and reach new audiences.

As long as marketers avoid making campaigns with malicious intent, deepfakes can help both the brand and the consumer by creating a more personalized, immersive experience.
More resources: Featured Image: Andrii Symonenko/Shutterstock

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Deepfake Technology Pros & Cons For Digital Marketing

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Deepfake technology is changing the way people see digital marketing. Though marketers are still in the early stages of experimenting with deepfakes and deepfake technology, these videos convey a more i mmersive marketing experience through storytelling. Deepfake technology is a type of "deep learning."

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What is a deepfake? Everything you need to know about the AI-powered fake media

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Deepfakes use deep learning artificial intelligence to replace the likeness of one person with another in video and other digital media.

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The ethical questions that haunt facial-recognition research

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The ethical questions that haunt facial-recognition research

# data[].attributes.body
Main idea: Four researchers wrote to the publisher wiley to " repectfully ask" that it immediately retract a scientific paper. The study, published in 2018, had trained algorithms to distinguished faces of Uyghur people.

Arguments/facts:
- In september 2019, four reserchas wrote to the publisher.
- China had already been internationally condemned
- Many researchers found it disturbing the academics.

Dialogue: I think all this post with the dates are very important.

Summary:
China had already been internatinally condemned for its heavy surveillance and mass detentions of Uyhurs. Many researchers found it disturbing that academics had tried to build such algorithms.

APA:
Richard Van Noorden ( 18 November 2020). The ethical questions that haunt facial-recognition research. https://www.nature.com/articles/d41586-020-03187-3

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In September 2019, four researchers wrote to the publisher Wiley to "respectfully ask" that it immediately retract a scientific paper. The study, published in 2018, had trained algorithms to distinguish faces of Uyghur people, a predominantly Muslim minority ethnic group in China, from those of Korean and Tibetan ethnicity.

China had already been internationally condemned for its heavy surveillance and mass detentions of Uyghurs in camps in the northwestern province of Xinjiang - which the government says are re-education centres aimed at quelling a terrorist movement. According to media reports, authorities in Xinjiang have used surveillance cameras equipped with software attuned to Uyghur faces.

As a result, many researchers found it disturbing that academics had tried to build such algorithms - and that a US journal had published a research paper on the topic. And the 2018 study wasn't the only one: journals from publishers including Springer Nature, Elsevier and the Institute of Electrical and Electronics Engineers (IEEE) had also published peer-reviewed papers that describe using facial recognition to identify Uyghurs and members of other Chinese minority groups. ( Nature 's news team is editorially independent from its publisher, Springer Nature.)

The complaint, which launched an ongoing investigation, was one foray in a growing push by some scientists and human-rights activists to get the scientific community to take a firmer stance against unethical facial-recognition research. It's important to denounce controversial uses of the technology, but that's not enough, ethicists say. Scientists should also acknowledge the morally dubious foundations of much of the academic work in the field - including studies that have collected enormous data sets of images of people's faces without consent, many of which helped hone commercial or military surveillance algorithms. ( A linked feature explores concerns over algorithmic bias in facial-recognition systems.)

An increasing number of scientists are urging researchers to avoid working with firms or universities linked to unethical projects, to re-evaluate how they collect and distribute facial-recognition data sets and to rethink the ethics of their own studies. Some institutions are already taking steps in this direction. In the past year, several journals and an academic conference have announced extra ethics checks on studies.

"A lot of people are now questioning why the computer-vision community dedicates so much energy to facial-recognition work when it's so difficult to do it ethically," says Deborah Raji, a researcher in Ottawa who works at the non-profit Internet foundation Mozilla. "I'm seeing a growing coalition that is just against this entire enterprise."

This year, Nature asked 480 researchers around the world who work in facial recognition, computer vision and artificial intelligence (AI) for their views on thorny ethical questions about facial-recognition research. The results of this first-of-a-kind survey suggest that some scientists are concerned about the ethics of work in this field - but others still don't see academic studies as problematic.

Data without consent

For facial-recognition algorithms to work well, they must be trained and tested on large data sets of images, ideally captured many times under different lighting conditions and at different angles. In the 1990s and 2000s, scientists generally got volunteers to pose for these photos - but most now collect facial images without asking permission.

For instance, in 2015, scientists at Stanford University in California published a set of 12,000 images from a webcam in a San Francisco café that had been live-streamed online. The following year, researchers at Duke University in Durham, North Carolina, released more than 2 million video frames (85 minutes) of footage of students walking on the university campus.

The biggest collections have been gathered online. In 2016, researchers at the University of Washington in Seattle posted a database, called MegaFace, of 3.3 million photos from the image-sharing site Flickr ⁴. And scientists at Microsoft Research in Redmond, Washington, issued the world's largest data set, MSCeleb, consisting of 10 million images of nearly 100,000 individuals, including journalists, musicians and academics, scraped from the Internet.

In 2019, Berlin-based artist Adam Harvey created a website called MegaPixels that flagged these and other data sets. He and another Berlin-based technologist and programmer, Jules LaPlace, showed that many had been shared openly and used to evaluate and improve commercial surveillance products. Some were cited, for instance, by companies that worked on military projects in China. "I wanted to uncover the uncomfortable truth that many of the photos people posted online have an afterlife as training data," Harvey says. In total, he says he has charted 29 data sets, used in around 900 research projects. Researchers often use public Flickr images that were uploaded under copyright licences that allow liberal reuse.

After The Financial Times published an article on Harvey's work in 2019, Microsoft and several universities took their data sets down. Most said at the time - and reiterated to Nature this month - that their projects had been completed or that researchers had requested that the data set be removed. Computer scientist Carlo Tomasi at Duke University was the sole researcher to apologize for a mistake. In a statement two months after the data set had been taken down, he said he had got institutional review board (IRB) approval for his recordings - which his team made to analyse the motion of objects in video, not for facial recognition. But the IRB guidance said he shouldn't have recorded outdoors and shouldn't have made the data available without password protection. Tomasi told Nature that he did make efforts to alert students by putting up posters to describe the project.

The removal of the data sets seems to have dampened their usage a little, Harvey says. But big online image collections such as MSCeleb are still distributed among researchers, who continue to cite them, and in some cases have re-uploaded them or data sets derived from them. Scientists sometimes stipulate that data sets should be used only for non-commercial research - but once they have been widely shared, it is impossible to stop companies from obtaining and using them.

In October, computer scientists at Princeton University in New Jersey reported identifying 135 papers that had been published after the Duke data set had come down and which had used it or data derived from it (see go.nature.com/3nlkjre). The authors urged researchers to set more restrictions on the use of data sets and asked journals to stop accepting papers that use data sets that had been taken down.

Legally, it is unclear whether scientists in Europe can collect photos of individuals' faces for biometric research without their consent. The European Union's vaunted General Data Protection Regulation (GDPR) does not provide an obvious legal basis for researchers to do this, reported Catherine Jasserand, a biometrics and privacy-law researcher at the Catholic University of Leuven in Belgium, in 2018. But there has been no official guidance on how to interpret the GDPR on this point, and it hasn't been tested in the courts. In the United States, some states say it is illegal for commercial firms to use a person's biometric data without their consent; Illinois is unique in allowing individuals to sue over this. As a result, several firms have been hit with class-action lawsuits.

The US social-media firm Facebook, for instance, agreed this year to pay US$650 million to resolve an Illinois class-action lawsuit over a collection of photos that was not publicly available, which it used for facial recognition (it now allows users to opt out of facial-recognition tagging). The controversial New York City-based technology company Clearview AI - which says it scraped three billion online photos for a facial-recognition system - has also been sued for violating this law in pending cases. And the US tech firms IBM, Google, Microsoft, Amazon and FaceFirst were also sued in Illinois for using a data set of nearly one million online photos that IBM released in January 2019; IBM removed it at around the time of the lawsuit, which followed a report by NBC News detailing photographers' disquiet that their pictures were in the data set.

Microsoft told Nature that it has filed to dismiss the case, and Clearview says it "searches only publicly available information, like Google or any other search engine". Other firms did not respond to requests for comment.

Vulnerable populations

In the study on Uyghur faces published by Wiley, the researchers didn't gather photos from online, but said they took pictures of more than 300 Uyghur, Korean and Tibetan 18-22-year-old students at Dalian Minzu University in northeast China, where some of the scientists worked. Months after the study was published, the authors added a note to say that the students had consented to this. But the researchers' assertions don't assuage ethical concerns, says Yves Moreau, a computational biologist at the Catholic University of Leuven. He sent Wiley a request to retract the work last year, together with the Toronto-based advocacy group Tech Inquiry.

It's unlikely that the students were told enough about the purpose of the research to have given truly informed consent, says Moreau. But even if they did freely consent, he argues, human-rights abuses in Xinjiang mean that Wiley ought to retract the study to avoid giving the work academic credence.

Moreau has catalogued dozens of papers on Uyghur populations, including facial-recognition work and studies that gathered Uyghur people's DNA. In December, he wrote an opinion article in Nature calling for all unethical work in biometric research to be retracted.

His campaign has had some impact, but not quite to the extent he'd hoped. Publishers say the key issue is checking whether participants in studies gave informed consent. Springer Nature, for instance, said in December 2019 that it would investigate papers of concern on vulnerable groups along these lines, and that it had updated its guidance to editors and authors about the need to gain explicit and informed consent in studies that involve clinical, biomedical or biometric data from people. This year, the publisher retracted two papers on DNA sequencing, because the authors conceded that they hadn't asked Uyghur people for their consent, and it has placed expressions of concern on 28 others.

Wiley has also focused on informed consent. Last November, the publisher told Moreau and Tech Inquiry that it was satisfied that consent forms and university approval for the Dalian study were available, and so it stood by the research, which it felt could be firmly separated from the actions in Xinjiang. "We are aware of the persecution of the Uyghur communities," Wiley said. "However, this article is about a specific technology and not an application of that technology."

In December, however, the publisher opened a formal investigation, after Curtin University in Perth, Australia, where one of the authors is based, also asked for a retraction, saying it agreed that the work was ethically indefensible. This year, Wiley added a publisher's note saying that the article "does appear to be in compliance with acceptable standards for conducting human subject research". In September, after Moreau dug into the authors' previous studies of facial recognition on Uyghurs and pointed out apparent inconsistencies in the year that the data sets had been gathered, Wiley placed an expression of concern on the study, saying that it was not clear when the data collection had taken place.

The publisher told Nature that it now considers the matter closed after thorough investigation - but not everyone involved is content. "Curtin University maintains that the paper should be retracted," deputy vice-chancellor Chris Moran told Nature. He said the university was still investigating the work.

Wiley says that after its conversations with Moreau, it updated its integrity guidelines to make sure that expected standards for informed consent are met and described in articles. Other publishers say that they have made adjustments, too. The IEEE says that this September, it approved a policy under which authors of articles on research involving human subjects or animals should confirm whether they had approval from an IRB or equivalent local review; editors determine whether research (on biometrics or other areas) involves human subjects.

But Moreau says that publishers' focus on the details of consent is too narrow, and that they should also take a stand on the wider ethics of research. "We are talking about massive human-rights abuses," he says. "At some point, Western publishers should say that there are some baselines above which they don't go." He suggests that publishers should set up independent ethics boards that can give opinions when questions such as these arise. (No publishers asked by Nature said that they had taken this step.) Universities and researchers who disapprove of human-rights abuses could also do more to express this by dropping their associations with questionable technology firms, says Kate Crawford, co-director of the AI Now Institute at New York University.

In the past year, there has been growing scrutiny of universities' partnerships with companies or research programmes linked to mass surveillance in Xinjiang. The Massachusetts Institute of Technology (MIT) in Cambridge, for example, said it would review its relationship with the Hong Kong-based tech firm SenseTime after the US government - in the middle of a trade war with China - blacklisted the firm and other Chinese AI companies, such as Megvii in Beijing, over their alleged contributions to human-rights violations in Xinjiang. In 2018, SenseTime and MIT announced they had formed an "alliance on artificial intelligence"; MIT says that SenseTime had provided an undisclosed sum to the university without any restrictions on how it would be used, and that the university will not give it back.

Both Megvii and SenseTime contest the US blacklisting. SenseTime says its technology has "never been applied for any unethical purposes", and Megvii says it requires its clients "not to weaponize our technology or solutions or use them for illegal purposes".

Academic conferences have been contentious, too. The Chinese Conference on Biometrics Recognition (CCBR) was held in Xinjiang's capital, Ürümqi, in 2018. Anil Jain, a computer scientist at Michigan State University in East Lansing, sat on the conference's advisory board and travelled there to give a speech. Some AI researchers, including Toby Walsh at the University of New South Wales in Sydney, Australia, later criticized Jain for this in stories r eported by the New York City-based Coda magazine.

Coda magazine also noted that Springer Nature sponsored the conference; the company said its role was limited to publishing CCBR proceedings and that it had strengthened its requirements for conference organizers to comply with the publisher's editorial policies after concerns were raised about past content. And Jain challenged the critique, telling Nature that attending conferences in China "does not mean that ... international conference participants, like me, condone these atrocities against minorities". Growth in surveillance there shouldn't be a reason to "curtail scientific exchange", he said.

Jain remains on the advisory board for CCBR 2020-21; Springer Nature is still publishing the conference abstracts. And major international computer-vision conferences have continued to accept sponsorship from Chinese firms. Just after the blacklisting, SenseTime and Megvii sponsored the 2019 International Conference on Computer Vision, and Megvii sponsored the 2020 Conference on Computer Vision and Pattern Recognition, although its logo was removed from the conference's website after the meeting occurred. "Conferences should avoid sponsors who are accused of enabling abuses of human rights," reiterates Walsh. However, he notes that last year, the non-governmental organization Human Rights Watch in New York City withdrew initial allegations that Megvii facial-recognition technology was involved in an app used in Xinjiang. Conference organizers did not respond to a request for comment.

Ethical checkpoints

Questionable research projects have popped up in the United States, too. On 5 May, Harrisburg University in Pennsylvania posted a press release declaring that researchers there had developed facial-recognition software "capable of predicting whether someone is likely going to be a criminal", with "80 percent accuracy and no racial bias". The announcement triggered a wave of criticism, as had previous studies that hark back to the discredited work of nineteenth-century physiognomists. One notorious 2016 study reported that a machine-learning algorithm could spot the difference between images of non-criminals and those of convicted criminals that were supplied by a Chinese police department.

Harrisburg University removed its press release on 6 May following the outcry, but left a dangling question: the press release had said that the work was to be published by Springer Nature in a book series (which the publisher later denied). On 22 June, more than 2,400 academics signed a letter from a group called the Coalition for Critical Technology (CCT), asking Springer Nature not to publish the work and calling on all publishers to refrain from publishing similar studies.

The letter pointed out that such studies are based on unsound science. It also noted that algorithmic tools that tell police where or who to target tend to provide a scientific veneer for automated methods that only exacerbate existing biases in the criminal justice system. Three days earlier, more than 1,400 US mathematicians had written a letter asking their colleagues to stop collaborating with police on algorithms that claim to help reduce crime, because of concerns about systemic racism in US law-enforcement agencies.

Springer Nature said the work was never accepted for publication: it had been submitted to a conference and rejected after peer review. (The authors, and Harrisburg University, declined to comment.)

Springer Nature was already under fire for a different paper, published in January in the Journal of Big Data, on detecting 'criminal tendency' in photos of criminals and non-criminals. After researchers from the IEEE got in touch with ethical concerns, Margeret Hall, the paper's co-author at the University of Nebraska Omaha, asked in June for the paper to be withdrawn. Hall says the now-retracted paper was "indefensible". Springer Nature says the journal reviewed its processes and now requires authors to include statements on ethics approvals and consent when submitting manuscripts.

Nature survey

To get a wider sense of academic views on facial-recognition ethics, Nature this year surveyed 480 researchers who have published papers on facial recognition, AI and computer science. On some questions, respondents showed a clear preference. When asked for their opinions on studies that apply facial-recognition methods to recognize or predict personal characteristics (such as gender, sexual identity, age or ethnicity) from appearance, around two-thirds said that such studies should be done only with the informed consent of those whose faces were used, or after discussion with representatives of groups that might be affected (see 'Facial recognition: a survey on ethics').

But on other issues, academics were split. Around 40% of the scientists in the survey felt that researchers should get informed consent from individuals before using their faces in a facial-recognition data set, but more than half felt that this wasn't necessary. The researchers' dilemma is that it's hard to see how they can train accurate facial-recognition algorithms without vast data sets of photos, says Sébastien Marcel, who leads a biometrics group at the Idiap Research Institute in Martigny, Switzerland. He thinks that researchers should get informed consent - but in practice, they don't. His own group doesn't crawl the web for images, but it does use online image data sets that others have compiled. "A lot of researchers don't want to hear about this: they consider it not their problem," he says.

Ed Gerstner, director of journal policy at Springer Nature, said the publisher was considering what it could do to discourage the "continued use" of image databases that don't have explicit consent for their use in research from the people in the images.

Nature 's survey also asked researchers whether they felt that facial-recognition research on vulnerable populations - such as refugees or minority groups that were under heavy surveillance - could be ethically questionable, even if scientists had gained informed consent. Overall, 71% agreed; some noted it might be impossible to determine whether consent from vulnerable populations was informed, making it potentially valueless.

Some of those who disagreed, however, tried to draw a distinction between academic research and how facial recognition is used. The focus should be on condemning and restricting unethical applications of facial recognition, not on restricting research, they said.

Ethicists regard that distinction as naive. "That's the 'I'm just an engineer' mentality - and we're well past that now," says Karen Levy, a sociologist at Cornell University in Ithaca, New York, who works on technology ethics.

Some of the respondents in China said that they were offended by the question. "You should not say that in Xinjiang some groups are detained in camps," wrote one. Just under half of the 47 Chinese respondents felt that studies on vulnerable groups could be ethically questionable even if scientists had gained consent, a lower proportion than respondents from the United States and Europe (both above 73%).

One Chinese American AI researcher who didn't want to be named said that a problem was a cultural split in the field. "The number of Chinese researchers at top conferences who actively support censorship and Xinjiang concentration camp[s] concerns me greatly. These groups have minimal contact with uncensored media and tend to avoid contact with those who don't speak Mandarin, especially about social issues like this. I believe we need to find ways to actively engage with this community," they wrote.

Nature asked researchers what the scientific community should do about ethically questionable studies. The most popular answer was that during peer review, authors of facial-recognition papers should be asked explicitly about the ethics of their studies. The survey also asked whether research that uses facial-recognition software should require prior approval from ethics bodies, such as IRBs, that consider research with human subjects. Almost half felt it should, and another quarter said it depended on the research.

Ethical reflection

Researchers who work on technology that recognizes or analyses faces point out that it has many uses, such as to find lost children, track criminals, access smartphones and cash machines more conveniently, help robots to interact with humans by recognizing their identities and emotions and, in some medical studies, to help diagnose or remotely track consenting participants. "There are a number of lawful and legitimate applications of face and biometric recognition which we need in our society," says Jain.

But researchers must also recognize that a technology that can remotely identify or classify people without their knowledge is fundamentally dangerous - and should try to resist it being used to control or criminalize people, say some scientists. "The AI community suffers from not seeing how its work fits into a long history of science being used to legitimize violence against marginalized people, and to stratify and separate people," says Chelsea Barabas, who studies algorithmic decision-making at MIT and helped to form the CCT this year. "If you design a facial-recognition algorithm for medical research without thinking about how it could be used by law enforcement, for instance, you're being negligent," she says.

Some organizations are starting to demand that researchers be more careful. One of the AI field's premier meetings, the NeurIPS (Neural Information Processing Systems) conference, is requiring such ethical considerations for the first time this year. Scientists submitting papers must add a statement addressing ethical concerns and potential negative outcomes of their work. "It won't solve the problem, but it's a step in the right direction," says David Ha, an AI researcher at Google in Tokyo. The journal Nature Machine Intelligence is also trialling an approach in which it asks the authors of some machine-learning papers to include a statement considering broader societal impacts and ethical concerns, Gerstner says.

Levy is hopeful that academics in facial recognition are waking up to the implications of what they work on - and what it might mean for their reputation if they don't root out ethical issues in the field. "It feels like a time of real awakening in the science community," she says. "People are more acutely aware of the ways in which technologies that they work on might be put to use politically - and they feel this viscerally."
doi: https://doi.org/10.1038/d41586-020-03187-3

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The ethical questions that haunt facial-recognition research

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Journals and researchers are under fire for controversial studies using this technology. And a Nature survey reveals that many researchers in this field think there is a problem. Journals and researchers are under fire for controversial studies using this technology. And a Nature survey reveals that many researchers in this field think there is a problem.

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Main idea:
Who is making Deepfakes?

Arguments/facts:
- AI can create really convincing fictional photos from scratch
- The government can use this to their advantage (e.g. To lie about rival countries, etc.)
- You need a high end desktop to create a convincing DeepFake

Dialogue:
DeepFakes can be very beneficial to some people, but they could spread fake news which will break everyone's trust. The good thing is you need a high-end computer to create a decent one.

Summary:
First, you run thousands of face shots of the two people through an AI algorithm called an encoder. The encoder finds and learns similarities between the two faces, and reduces them to their shared common features, compressing the images in the process. A second AI algorithm called a decoder is then taught to recover the faces from the compressed images. Because the faces are different, you train one decoder to recover the first person’s face.

APA

No name, (Mon 13, Jan 2020). What are deepfakes – and how can you spot them? The Guardian. Retrieved from https://www.theguardian.com/technology/2020/jan/13/what-are-deepfakes-and-how-can-you-spot-them

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What is a deepfake?

Have you seen Barack Obama call Donald Trump a " complete dipshit", or Mark Zuckerberg brag about having " total control of billions of people's stolen data", or witnessed Jon Snow's moving apology for the dismal ending to Game of Thrones? Answer yes and you've seen a deepfake. The 21st century's answer to Photoshopping, deepfakes use a form of artificial intelligence called deep learning to make images of fake events, hence the name deepfake. Want to put new words in a politician's mouth, star in your favourite movie, or dance like a pro? Then it's time to make a deepfake.

What are they for?

Many are pornographic. The AI firm Deeptrace found 15,000 deepfake videos online in September 2019, a near doubling over nine months. A staggering 96% were pornographic and 99% of those mapped faces from female celebrities on to porn stars. As new techniques allow unskilled people to make deepfakes with a handful of photos, fake videos are likely to spread beyond the celebrity world to fuel revenge porn. As Danielle Citron, a professor of law at Boston University, puts it: "Deepfake technology is being weaponised against women." Beyond the porn there's plenty of spoof, satire and mischief.

Is it just about videos?

No. Deepfake technology can create convincing but entirely fictional photos from scratch. A non-existent Bloomberg journalist, "Maisy Kinsley", who had a profile on LinkedIn and Twitter, was probably a deepfake. Another LinkedIn fake, "Katie Jones", claimed to work at the Center for Strategic and International Studies, but is thought to be a deepfake created for a foreign spying operation.

Audio can be deepfaked too, to create " voice skins" or " voice clones" of public figures. Last March, the chief of a UK subsidiary of a German energy firm paid nearly £200,000 into a Hungarian bank account after being phoned by a fraudster who mimicked the German CEO's voice. The company's insurers believe the voice was a deepfake, but the evidence is unclear. Similar scams have reportedly used recorded WhatsApp voice messages.

A comparison of an original and deepfake video of Facebook chief executive Mark Zuckerberg. Photograph: The Washington Post via Getty Images

How are they made?

University researchers and special effects studios have long pushed the boundaries of what's possible with video and image manipulation. But deepfakes themselves were born in 2017 when a Reddit user of the same name posted doctored porn clips on the site. The videos swapped the faces of celebrities - Gal Gadot, Taylor Swift, Scarlett Johansson and others - on to porn performers.

It takes a few steps to make a face-swap video. First, you run thousands of face shots of the two people through an AI algorithm called an encoder. The encoder finds and learns similarities between the two faces, and reduces them to their shared common features, compressing the images in the process. A second AI algorithm called a decoder is then taught to recover the faces from the compressed images. Because the faces are different, you train one decoder to recover the first person's face, and another decoder to recover the second person's face. To perform the face swap, you simply feed encoded images into the "wrong" decoder. For example, a compressed image of person A's face is fed into the decoder trained on person B. The decoder then reconstructs the face of person B with the expressions and orientation of face A. For a convincing video, this has to be done on every frame.

Comparing original and deepfake videos of Russian president Vladimir Putin. Photograph: Alexandra Robinson/AFP via Getty Images

Another way to make deepfakes uses what's called a generative adversarial network, or Gan. A Gan pits two artificial intelligence algorithms against each other. The first algorithm, known as the generator, is fed random noise and turns it into an image. This synthetic image is then added to a stream of real images - of celebrities, say - that are fed into the second algorithm, known as the discriminator. At first, the synthetic images will look nothing like faces. But repeat the process countless times, with feedback on performance, and the discriminator and generator both improve. Given enough cycles and feedback, the generator will start producing utterly realistic faces of completely nonexistent celebrities.

Who is making deepfakes?

Everyone from academic and industrial researchers to amateur enthusiasts, visual effects studios and porn producers. Governments might be dabbling in the technology, too, as part of their online strategies to discredit and disrupt extremist groups, or make contact with targeted individuals, for example.

What technology do you need?

It is hard to make a good deepfake on a standard computer. Most are created on high-end desktops with powerful graphics cards or better still with computing power in the cloud. This reduces the processing time from days and weeks to hours. But it takes expertise, too, not least to touch up completed videos to reduce flicker and other visual defects. That said, plenty of tools are now available to help people make deepfakes. Several companies will make them for you and do all the processing in the cloud. There's even a mobile phone app, Zao, that lets users add their faces to a list of TV and movie characters on which the system has trained.

Chinese face-swapping app Zao has caused privacy concerns. Photograph: Imaginechina/SIPA USA/PA Images

How do you spot a deepfake?

It gets harder as the technology improves. In 2018, US researchers discovered that deepfake faces don't blink normally. No surprise there: the majority of images show people with their eyes open, so the algorithms never really learn about blinking. At first, it seemed like a silver bullet for the detection problem. But no sooner had the research been published, than deepfakes appeared with blinking. Such is the nature of the game: as soon as a weakness is revealed, it is fixed.

Poor-quality deepfakes are easier to spot. The lip synching might be bad, or the skin tone patchy. There can be flickering around the edges of transposed faces. And fine details, such as hair, are particularly hard for deepfakes to render well, especially where strands are visible on the fringe. Badly rendered jewellery and teeth can also be a giveaway, as can strange lighting effects, such as inconsistent illumination and reflections on the iris.

Governments, universities and tech firms are all funding research to detect deepfakes. Last month, the first Deepfake Detection Challenge kicked off, backed by Microsoft, Facebook and Amazon. It will include research teams around the globe competing for supremacy in the deepfake detection game.

Facebook last week banned deepfake videos that are likely to mislead viewers into thinking someone "said words that they did not actually say", in the run-up to the 2020 US election. However, the policy covers only misinformation produced using AI, meaning "shallowfakes" (see below) are still allowed on the platform.

A woman watches a deepfake video of Donald Trump and Barack Obama. Photograph: Rob Lever/AFP via Getty Images

Will deepfakes wreak havoc?

We can expect more deepfakes that harass, intimidate, demean, undermine and destabilise. But will deepfakes spark major international incidents? Here the situation is less clear. A deepfake of a world leader pressing the big red button should not cause armageddon. Nor will deepfake satellite images of troops massing on a border cause much trouble: most nations have their own reliable security imaging systems.

There is still ample room for mischief-making, though. Last year, Tesla stock crashed when Elon Musk smoked a joint on a live web show. In December, Donald Trump flew home early from a Nato meeting when genuine footage emerged of other world leaders apparently mocking him. Will plausible deepfakes shift stock prices, influence voters and provoke religious tension? It seems a safe bet.

Will they undermine trust?

The more insidious impact of deepfakes, along with other synthetic media and fake news, is to create a zero-trust society, where people cannot, or no longer bother to, distinguish truth from falsehood. And when trust is eroded, it is easier to raise doubts about specific events.

Last year, Cameroon's minister of communication dismissed as fake news a video that Amnesty International believes shows Cameroonianthe country's soldiers executing civilians.

Donald Trump, who admitted to boasting about grabbing women's genitals in a recorded conversation, later suggested the tape was not real. In Prince Andrew's BBC interview with Emily Maitlis, the prince cast doubt on the authenticity of a photo taken with Virginia Giuffre, a shot her attorney insists is genuine and unaltered.

"The problem may not be so much the faked reality as the fact that real reality becomes plausibly deniable," says Prof Lilian Edwards, a leading expert in internet law at Newcastle University.

As the technology becomes more accessible, deepfakes could mean trouble for the courts, particularly in child custody battles and employment tribunals, where faked events could be entered as evidence. But they also pose a personal security risk: deepfakes can mimic biometric data, and can potentially trick systems that rely on face, voice, vein or gait recognition. The potential for scams is clear. Phone someone out of the blue and they are unlikely to transfer money to an unknown bank account. But what if your "mother" or "sister" sets up a video call on WhatsApp and makes the same request?

What's the solution?

Ironically, AI may be the answer. Artificial intelligence already helps to spot fake videos, but many existing detection systems have a serious weakness: they work best for celebrities, because they can train on hours of freely available footage. Tech firms are now working on detection systems that aim to flag up fakes whenever they appear. Another strategy focuses on the provenance of the media. Digital watermarks are not foolproof, but a blockchain online ledger system could hold a tamper-proof record of videos, pictures and audio so their origins and any manipulations can always be checked.

Are deepfakes always malicious?

Not at all. Many are entertaining and some are helpful. Voice-cloning deepfakes can restore people's voices when they lose them to disease. Deepfake videos can enliven galleries and museums. In Florida, the Dalí museum has a deepfake of the surrealist painter who introduces his art and takes selfies with visitors. For the entertainment industry, technology can be used to improve the dubbing on foreign-language films, and more controversially, resurrect dead actors. For example, the late James Dean is due to star in Finding Jack, a Vietnam war movie.

What about shallowfakes?

Coined by Sam Gregory at the human rights organisation Witness, shallowfakes are videos that are either presented out of context or are doctored with simple editing tools. They are crude but undoubtedly impactful. A shallowfake video that slowed down Nancy Pelosi's speech and made the US Speaker of the House sound slurred reached millions of people on social media.

In another incident, Jim Acosta, a CNN correspondent, was temporarily banned from White House press briefings during a heated exchange with the president. A shallowfake video released afterwards appeared to show him making contact with an intern who tried to take the microphone off him. It later emerged that the video had been sped up at the crucial moment, making the move look aggressive. Acosta's press pass was later reinstated.

The UK's Conservative party used similar shallowfake tactics. In the run-up to the recent election, the Conservatives doctored a TV interview with the Labour MP Keir Starmer to make it seem that he was unable to answer a question about the party's Brexit stance. With deepfakes, the mischief-making is only likely to increase. As Henry Ajder, head of threat intelligence at Deeptrace, puts it: "The world is becoming increasingly more synthetic. This technology is not going away."

This article was amended on 3 March 2023 to correct a misspelling of Jim Acosta's surname.

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What are deepfakes - and how can you spot them?

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AI-generated fake videos are becoming more common (and convincing). Here's why we should be worried

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This shows how easy it is to change up someones appearances to seem like another person

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Main idea:
Will this break everyone's trust?

Arguments/facts:
- Legislation can be used to regulate the use of deepfakes
- Deepfakes allow anyone's image to be co-opted
- There could even be public figures who are entirely imaginary and we don't know it

Dialogue:
Deepfakes will probably create a zero-trust society, where everyone is sceptical about what is posted on the internet. You'll likely raise doubts about specific news whenever you see something on the internet.

Summary:
For this to happen, lots of re-occurring deepfakes have to appear on the internet and be exposed as fake ones, which will definitely undermine/break trust in people, which will probably make most people not use the internet anymore

APA
Benjamin, Garfield. (6th, Febuary 2019). Deepfake videos could destroy trust in society – here’s how to restore it. THECONVERSATION. Retrieved from: https://theconversation.com/deepfake-videos-could-destroy-trust-in-society-heres-how-to-restore-it-110999

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Main idea:
Explain us a big part of the benefits of deepfakes
Arguments/facts:
-Deepfakes push the boundary of IA technology
-A blessing for the entretaiment Industry
-Deep fakes teach people to be critical thinkers

Dialogue: I diden't know that deepfakes created Alexa,siri etc...

Summary:
Tells us about 10 importnants benefits of deepfakes that we can use in our daily life, as well explain us that we are using benefits in deepfakes in our every day life time.

APA:
Arnold. (21 abril 2021). 10 Reasons Why Deepfakes Can Be Good For This World. DeepfakeNow. Retrived from https://deepfakenow.com/10-reasons-why-deepfakes-can-be-good-for-this-world/

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10 Reasons Why Deepfakes Can Be Good For This World

You don't need to look far to find experts claiming that deepfakes will be the end of the world as we know it. The position that is explored much less by people, are the upsides and positives of Artificial Intelligence (AI) in relation to deepfake technology.

Because it is true: there are many positives to be found in the world of deepfakes.

Exploring these benefits can shed some light on the broader application of deep learning tech, as well as provide a more realistic and balanced approach to the deepfake problem as a whole.

Because it is very much true that deepfakes can be used for bad as well as a wide range of good causes. That is exactly why this article sets out to pinpoint ten clear benefits of deepfakes for people around the world.

Are There Only Negative Aspects To Deepfake Technology?

Different than what most experts and media outlets would lead us to believe, the advancement of deepfake technology is not all focused on negative impacts. In fact, there are some interesting cases to be made for the implementation of deep learning AI in media industries, the medical field, the technological field, or the improvement of user interfaces.

Notwithstanding the huge potential for negative side-effects of deepfake tech, these promising 'positively focused' applications might actually be of interest to a rather large number of people, in particular in the entertainment and technologically-oriented industries.

While we must not underestimate the potential of AI-generated deepfakes causing disturbances in the wider societal systems, it is at least worth considering to look at the positives as well. And there are plenty benefits to be found, we quickly found out after doing some research on the topic. Read on to discover some of the main takeaways of those potential beneficial applications.

The Positive Side Of Deepfakes

Emphasizing the potential positives of deepfakes is controversial, yet we attempted to compile a comprehensive list of the most likely beneficial effects of deepfake technology. While reading through the overview, remember to remain critical and verify with additional sources where needed. Please don't see the list below as an exhaustive list, but more as an indicator of potential benefits. That being said, here are our main ten possible benefits of deepfake technology as they stand today.

1. Deepfakes Push The Boundary Of AI Technology

AI technology and machine learning algorithms need to be fed data to learn. What better tool is there than feeding our AI-overlords a range of images to learn with?

Faking images, audio, or video all help machines to learn things about the world around them.

Visualize it like this: you're an incredibly smart person but don't have any sensory input. No eyes to see, no ears to hear, and no nose to smell. And suddenly - your creator comes along and gives you a set of images and videos to understand the world around you.

You'd be thrilled! More information to understand the fabric of space-time around you! And the more information you gather (let's call the internet the life-juice of AI), the better you get at functioning in the real world. The more data gets injected, the smarter the machines get.

It's an 'AI for dummies' explanation of what's actually happening and we are glossing over a lot of important and intricate details here, but the core concept is clear: deepfakes are a tool to help computers learn. The more it works, the more it learns. Just like humans that train for a marathon, doing things over and over again will make you better at the task at hand.

2. A Blessing For The Entertainment Industry

Another obvious benefit is the range of applications of deepfake technology in the entertainment industry. And it's not just limited to the adult entertainment ( deepnudes have been one of the trending themes in the deepfake tech world), but it can also open up a range of creative avenues for movie creators, animators, TV show creators, and so on.

Deepfakes no longer require movie creators to hide the faces of stunt doubles, just paste the actor's face over it using some machine learning tech, and you're all set! It could potentially save millions of dollars in the production of movies, as well as provide more creative freedom in the making of Hollywood blockbusters.

But also animators can use the tech to make real-world celebrities say anything they'd like for their short film, animation, or TV show. The creative limitations will vanish gradually, the more accessible deepfake tech becomes for the professional entertainment industry. When your video creation options are virtually limitless, the only limitation becomes the human imagination itself.

3. Deepfakes Can Bring Back The Dead (As A Hologram)

This may sound kind of scary, but what if you could talk to your deceased family members one more time? What would you say to them? What would that do to you emotionally? Would it be helpful in the grieving process?

Holograms are already hyper-realistic, but with the addition of deepfake technology, it would be theoretically possible to make anyone say anything. Let's say your grandmother recorded a bunch of audio tapes in the years before she died. Wouldn't it be wonderful to use her voice in a deepfake audio conversation? And use AI to make it seem like you'd actually talk to your grandmother again? There sure would be millions of people wanting to pay top dollar to talk to their deceased loved ones one more time.

And the opportunities aren't limited to your loved ones, either. Famous historical figures could also be a great conversation partner. A conversation with Albert Einstein about the universe? Sure thing. Ready to take a picture with Salvador Dali at a museum? Already exists. Want to talk about current-day politics with Adolf Hitler? Boot up the hologram, ramp up the deepfake meter and make him say whatever you'd like.

It's not difficult to imagine an entirely new industry emerging from this. The more media content you have captured about a person, the easier it is to create a hyper-realistic hologram replica of that person. The future of holograms just got way more interesting.

4. Deepfakes Teach People To Be Critical Thinkers

If everything is fake, you have to think twice to actually believe something that is suggested by the media as a fact. Critical thinking is a skill that should already be ingrained into all of us - it is a weapon against manipulation from all sources around us. It arms us against media manipulation, our peers, and other sources where framing can happen.

Deepfakes add another level of worry. We know stunts in movies can be fake, but real-world videos can now also be faked. So how should we react if we see any type of video in the media? We should be cautious believing it. Now more than ever.

Teaching critical thinking skills early on in the education system will become an essential part of our daily lives. Changing the educational system to facilitate this change will be key to counter the technological force of deepfakes.

5. Visual Storytelling

Why not bridge the gap between imagination and visualization a bit with some good old deepfake videos? Hiring a celebrity voice actor to narrate your show can be extremely expensive as well, so audio-wise, storytelling can also become a lot cheaper. For example, if you're making a nature documentary and want Sir David Attenborough to tell stories about the animals you're shooting, now you can with a little AI-magic.

We already briefly mentioned Salvador Dali, which is currently being used in a museum for entertainment purposes. It's obviously not the real celebrity, because Dali died in the year 1989. But imagine Madame Tussauds, the wax statue exhibit, using deepfake tech to enhance the public's experience. Wouldn't that be much more interesting than some dull static wax statues?

Visual storytelling can be done in so many ways. It's an industry in and of itself that extends into many branches of society. From education to art, and from entertainment to marketing. Anyone with the right set of tools can soon start to enhance their user experience through the use of deepfakes.

6. Healthcare Potential

Training AI has major health benefits. If you can train machines to find cat videos on YouTube, you can also train them to identify medical conditions with common symptoms. It's also a lot easier to use AI for spotting particularly rare diseases, which are often overlooked by human doctors, regardless of how experienced and professional they are.

But there isn't just an indirect correlation between deepfakes and the good it can do for the medical world. One professor expert in the field of AI, based in the University College London (UCL), has predicted that deep generated models can create a range of new possibilities for healthcare. For one, it would allow medical researchers to come up with helpful datasets to develop new methods of disease treatment without the need for test trials in human patients.

Another benefit would refer back to the holograms we discussed before: imagine a world with a shortage of medical professionals (let's say, another global pandemic happens). Wouldn't it be helpful to run through the symptoms with your local doctor, that is not actually physically present, but will help you as an AI-enhanced hologram? It sounds very futuristic and somewhat crazy, but technologically it is already very much possible, mostly thanks to advanced deepfake tech.

7. New Forms Of Artistic Expression

Zooming in on the world of art and culture, we can see a major benefit of the new possibilities this technology brings along. Entirely new categories of art can emerge from deepfakes. A talented artist with a good idea could easily build a career out of using deepfake tricks in his art style.

Artistic freedom through tools like deepfake technology is just one of the many potential benefits that is being explored right now, but concrete examples are already starting to pop up around the globe.

For example, deepfake-based voice software tools are currently being developed to allow artists to produce speech from text and edit it in a similar way you would alter images in photoshop. It's just one example, but allowing creators access to impressive and easy-to-use deepfake tools will emerge a new world of creative freedom in ways we would never have imaged. Just this aspect alone will change the internet as we know it.

8. Next Level 'Smart Assistants' (Like Siri And Alexa)

Let's be real, smart assistants like Apple's Siri, Google's Assistant, or Amazon's Alexa are a bit robotic. They don't actually feel human to their users. It's not a good user experience, because people identify with people, not with robots.

Using deepfake audio to make robots and software sound more realistic to the human ear will greatly enhance the immersion in the human-AI interaction. Deepfake audio allows developers to alter smart assistants or even robot assistants to gain more human traits, by feeding them human voices and teaching them human manners.

While the improvements in smart assistants will likely be at the forefront of this improvement, it won't be long before every household has their own personal robot butler, that's almost like a human pet to them. And guys, it's just software and algorithms without actual human emotions. Don't feel bad for your soon-to-be Mr. Robot Butler, please.

9. The Basis For A New Social Media Landscape

Platforms such as Facebook, Twitter, Instagram and LinkedIn are all social media version 1.0 - it's the first 'real' generation of social platforms on the internet. Okay, maybe MySpace can be in there as well. Inevitably, these older established (or already shut down) platforms will be replaced by newer, fancier ones. And we aren't talking about TikTok, that's part of the current generation.

In an advanced social media landscape, version 2.0 so to speak, things would look very different. Think about the movie Ready Player One, think about the video game Second Life, and think about VR Chat. That, but in an altered form, powered by deepfake tech. You only need to write and your virtual avatar will act it out. It could very well be the next generation of social media platforms.

Developing your own virtual persona will be a piece of cake with the tech. The tech could emerge a new social media worlds where we use an alternate persona. Perhaps a merge with the gaming world (a new form of MMO 's) could make things more interesting. After all, people love virtual rewards, buying in-game items for their persona, and do all sorts of things to interact with others. The social media landscape of the future could become very interesting with easy-access deepfakes.

10. Enhanced Communication Potential

Communication makes the world go round. Without it, the global economy would grind to a halt overnight. So making communication tools easier, more accessible, and better to understand will all massively contribute to the well-being of societies around the world. Deepfake technology could very well play a big role in this advancement in the upcoming decades.

There is currently a massive amount of progress in audio deepfakes, especially voice calls over the internet. It's an essential tool for scammers, but to every bad side of the coin there is also a good side. Improvements in audio-based deepfake technology can help enhance privacy in the communication world.

Privacy and communication are an immensely hot topic right now on the web, with the implementation of numerous privacy bills for the internet. Privacy laws like the European GDPR require business owners to find new tools for their customers to protect their internet privacy, and what better way to do this than actively using a deepfake voice for calls? It's just one of countless ideas one could have when it comes to communication enhancement. Making it easy for customers to shield their private life from the 'corporations' or 'the government' will become more relevant by the day. We'll likely see more of this in the near future.

Making The Most Of An AI-Manipulated World

Painting a doomsday-like picture around the topic of deepfake technology is commonplace throughout the media landscape. Whenever the developments are brought up, negative connotations arise instantly. But it does not need to always be that way.

Beneficial influences of deepfake technologies are more omnipresent than most people (even experts in the field) want to believe. Taking a step back and looking at the other side of the coin can help people to have a more realistic view of what a world with deepfakes could look like.

We hope the overview in this article has been helpful in providing the debate with an alternative view - one that is based on positivity and hope for the future. In the end, it is technology that will move us forward as a species, regardless of the risks that these technologies might eventually pose to all of us. Where there are risks - there are also opportunities. Let us embrace those more when it comes to the world of deepfakes.

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10 Reasons Why Deepfakes Can Be Good For This World - Deepfake Now

# data[].attributes.attachment_link.description
You don't need to look far to find experts claiming that deepfakes will be the end of the world as we know it. The position that is explored much less by people, are the upsides and positives of Artificial Intelligence (AI) in relation to deepfake...

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Main idea:
The main Idea is the good side of deepfakes.

Arguments/facts:
-They present plenty of opportunities for mischef and malicious use.
-We can use deepfakes to make videos fotos.
- With deepfakes we can learn machine learning and deep learning.

Dialogue: I think that its incredible that the bad part is completly different from the benefits of DeepFakes.

Summary:
Deepfakes can have a postive acts in the world as an educational tool for the following reasons: For instance it could revolutionise our history lessons with interactivity how? with deepfakes examples of hystorical figures.
APA:
No name. (no date).Yes, positive deepfake examples exist. Think Automatation. Retrieved from https://www.thinkautomation.com/bots-and-ai/yes-positive-deepfake-examples-exist/

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ThinkAutomation | Business Process Automation Software

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ThinkAutomation is a business process automation (BPA) solution designed to automate on-premises and cloud-based business processes that are triggered from incoming messages. Messages can be received by email, database records, webhooks, web forms, SMS messages, Teams messages, documents, local file…

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We sat down with our Chief Data Officer, Dave Costenaro, to get some details surrounding the popular topic of deepfakes. If you're interested in learning where they originated, how to protect against them, and what the future conversation might look like, this article has you covered and then some.

1. What are deepfakes?

A deepfake is a deep neural network that's very adaptive at generating, editing, or interpreting high bandwidth media. A deepfake attack could include editing or modifying an audio, video, or image file to alter its meaning. For example, a deep neural network can take someone's voice and apply a different style to it, so they sound like someone else.

Another tactic of deep faking includes taking a video of someone standing still and combining it with a video of someone dancing. This can make the dancing person stand still and vice versa.

2. How long have deepfakes been around?

Deep faking started in a dark corner of the internet in 2012. Today it's known for gimmicky memes, but also for serious trust and privacy violations. One of the most famous deepfakes includes the video of Mark Zuckerberg where his voice was dubbed over to sound and look like he is taunting Facebook users about access to personal data. There's also a deepfake video of Nancy Pelosi that slows down her voice and movements to make her sound drunk and disoriented.

There has been serious concern around the 2020 election since these clips of high-profile deepfakes have gone viral. As humans, the fast operating parts of our brain automatically anchors in a perception and easily snaps to judgment even if we know the video is completely fake. Because millions of people can be rapidly exposed to these deepfake videos, if a candidate is attacked, there's a possibility of groundless reputational damage and polling swings.

3. In a recent article, you were quoted warning companies to "beware of the impact that deepfakes will have on identity verification and security in general." What are the potential threats?

An unnamed UK-based energy firm was recently victim to a deepfake attack that spoofed the CEOs voice in a phone call to the CFO. In the call, the fake CEO seemingly asked the CFO to authorize a payment to a foreign supplier. The CFO did so, which cost the company a lot of money proving out a security risk scenario for organizations everywhere.

Companies with big troves of data are the most at risk. The most obvious security practice to put in place is two-factor authentication. Rather than requiring one-step verification like a phone call, organizations should also require a second communication from a trusted source before releasing funds or information. In the case of the phishing attack, if the CFO and the Chief Accountant, for example, had to authorize the money transfer, that attack may have been thwarted.

Companies with a large presence online should be conscious of PR exposure until device and platform providers have put security protocols in place to solve and detect against deepfakes.

4. What protocols do you think these providers could put in place?

I think setting up security protocols online would be the most efficient on the devices and platforms rather than expecting individuals and companies that use these devices to come up with their own strategies to defend against it. It's a very sophisticated problem that should be dealt with first and foremost at the platform level.

Currently, Facebook, Twitter, and LinkedIn all have teams that are focused on this topic and formulating plans to prevent it. There are several methods being researched to technologically address deepfakes like embedding digital watermarks or fingerprints, or using signatures inside file metadata. However, this often requires a file to be registered upfront, so viewers know that it's coming from a valid device.

In a recent twitter poll, I asked users if they would register their own digital fingerprint with social media apps to ensure posts in the network are authentic. About half said yes, while the rest was a mixed bag that included different variations of no:

No, I just won't post images or videos.

No, delete my account.

Idk, what is a deepfake?

Because it was a mixed bag, I concluded that platform providers wouldn't voluntarily put up security protocols for users because that would decrease user engagement, advertisement clicks, and profits. The government would likely need to step in with regulations if this approach were to move forward.

5. How can you detect deepfakes?

As mentioned previously, there's a lot of research going on in this area and they're trying to figure out more sophisticated ways to fight against it.

It used to be that cruder models were easier to detect, just like amateur photoshop jobs. However, like photoshop, techniques have advanced, so jaw movement, eye blinking, and face motion has become very smooth and realistic over time. Now it's important to mathematically analyze the subtle details and anomalies like the statistical distribution of color or the pacing of the voice.

Unfortunately, deepfakes are getting better too, and these differences are ever more subtle. At some point, they will probably become indistinguishable based on analyzing an image or audio. This is especially true if there are no security protocols put in place at the platform level.

6. Do you see the conversation around deepfakes changing?

Yes. But, I don't think we've seen a level-10 emergency deepfake that has really captured the public's attention or outrage. I hope that doesn't happen, but it hasn't become a first-tier concern. If that does happen, the conversation will become more prominent and I think people will want to regulate it.

If a level-10 emergency doesn't happen, I think the conversation will stay where it is. There will continue to be isolated companies and individuals that are attacked by deepfakes and maybe a new insurance market will develop to defend against deepfake losses. Ultimately, it's an arms race against the deepfakers and the detectors.

As technology advances, so do the risks. For organizations or individuals, it's important to keep security practices top of mind when working with any vendor.

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6 Things You Probably Didn't Know About Deepfakes

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We sat down with our Chief Data Officer, Dave Costenaro, to get some details surrounding the unanswered questions about deepfakes.