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How to make deep fake video: A guide + warnings

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How to make deep fake video: A guide + warnings

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Main Idea: Deepfakes are incredibly hard to produce and require lots of technical expertise to produce a ultra realistic deepfake.

Arguments/facts:

It takes powerful hardware to be able to produce a deepfake

You need an AI encoder

Summary:
It is hard to make a deepfake, but extremely difficult to produce a really hyper-realistic deepfake.
Source: https://www.knowyourmobile.com/user-guides/how-to-make-deep-fake-video-a-guide-warnings/

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Deep fake videos are becoming increasingly popular online. Plenty of people believe they're dangerous. But how do you make a deep fake video?
Before AI, we had Photoshop. And during most of the 2000s and the 2010s, this was how fake stuff was made. You edited photos to look like something else. And depending on your Photoshop skills, they were either really impressive or rubbish.

With the rise of AI, there is now a new tool available for people that want to make fake videos. It's called "deep fake" and you've probably seen one or two of them already - a really popular one is a Tom Cruise deep fake.

Others include Jon Snow apologizing for the Game of Thrones ending, and Barack Obama calling Trump a "Dipsh*t".

The VAST majority of deep fake videos are in nature. According to a report from Deeptrace, upwards of 96% of all deep fake videos are pornographic in nature - and the number has doubled during the first nine months of 2021. 99% of the videos were also of female celebrities.

The History of Deep Fake

Hollywood has been pushing the boundaries of what is possible with visual effects for decades. Recent innovations like de-aging, which was used extensively on The Irishman, making Robert De Niro, Al Pacino, and Joe Pesci look decades younger, are just one example of how reality can be warped using computer technology.

Hollywood isn't behind the current glut of deep fake videos you'll find online; instead, it is savvy computer geeks with, arguably, way too much time on their hands. Deep fakes - as we know them - were born on 2017 and initially published on Reddit by a user called Deep Fake.

These deep fake videos were, once again, pornographic in nature and featured female celebrities like Gal Gadot, Taylor Swift, and Scarlett Johansson. It was these initial NSFW videos that first brought the concept of deep fake to the masses. From here, deep fake videos grew and grew, covering everything from parodies to satire and literally everything else in between.

How Do You Make A Deep Fake Video

The "professional" deep fake videos that are most popular online are the product of hundreds of hours of work, and a heck of a lot of computing power. Most deep fake videos - at least, the most convincing ones - are made in one of two ways:

Using An AI Encoder

With this method, you feed thousands of photos of two faces into an AI algorithm called an encoder. The encoder then finds similarities between the two, things that match effectively. It then starts compressing the images.

This is when the second algorithm comes into play; it is called an encoder. And the encoder's job is to recover the faces from the compressed files. But when you're making deep fakes, you use two encoders and you get one to recover one face and the other encoder to recover the other one.

The magic happens when you feed the wrong face into the wrong encoder, so FACE A is fed into encoder B, and FACE B is fed into encoder A. The encoder will then reconstruct FACE A with the expressions and orientations of FACE B. This has to be done on every frame in the video, however, so it is insanely CPU intensive.

Using A General Adversarial Network (GAN)

Making deep fakes using a GAN involves pitting two AI algorithms against one another, hence the term Adversarial. In this method, one AI, referred to as the generator, is fed random noise which is turned into an image. This "image": which is completely random at first, is then added to a stream of real images - a celebrity in this context - which is called the discriminator.

Initially, the discriminator's feed will look nothing like the celebrity. It will be a mess, looking random and unlike anything human. But after a few hundred cycles, the AI starts to work its magic, and eventually, you'll have yourself a believable, realistic deep fake video. Again, this method is very CPU-intensive and requires a lot of expert knowledge and hardware.

Is It Hard To Make Deep Fake Videos?

To make really good deep fake videos, yes, it is incredibly hard. You need a powerful computer, complex software, knowledge of using AI and machine learning, and editing skills. All of these things are required in equal measure to make a believable-looking deep fake video.

As with most things, however, there is an app you can run on your phone for making deep fake videos - it is called Zao. With the Zao app, you have a bunch of preinstalled celebrity faces. Once you have the app installed, you can then swap your face for one of the celebrities.

You can even commission companies to make deep fake videos for you as well. These firms have access to the best hardware and software. But it isn't cheap or advisable - deep fake videos make authorities very nervous. And the reason is simple: they warp reality and can cause panic, mass hysteria, and be used to push fake news.

How To Spot A Deep Fake

If you've seen The Irishman, you'll understand that while De Niro and Pacino do look pretty realistic in the movie as younger men, there is still something "wrong" and "unnatural" about their faces. They just look a bit, well... synthetic and/or artificial. And the eyes never look right.

And with deep fake videos, it is much the same; they look fairly believable in some cases but they all have an undeniable air of unreality about them - they just don't look 100% right. This is the easiest way to spot a deep fake.

Does it look real? Does it give you an uneasy feeling? The human mind is incredibly discerning, it can immediately spot "errors" or things that aren't quite right. It could be the way it moves or the eyes, or barely discernible artifacts.

Whatever it is, deep fakes usually have plenty of tells. When deep fakes first arrived on the scene, it was really easy to spot them. They didn't blink, as the images were pulled from still images, but no sooner was this noted, deep fakes with blinking eyes appeared. What does this mean? Simple: deep fakes will get harder and harder to spot.

Tech firms like Facebook and Microsoft are now actively working on AI systems that can instantly spot deep fakes as soon as they're published. Once these new AI systems roll out, it will become harder and harder for deep fakers to publish content online.

Will it kill deep fakes entirely? No, but it will make life harder for those that use deep fakes for nefarious purposes like spreading false information and/or defaming celebrities.

With deep fakes though the genie is well and truly out the bottle. Tech firms can do all they want to try and block them, but when Hollywood and political parties are using the technology, there's always going to be copycats and those that use deep fake technology for the wrong reasons.

Reality has never been more synthetic, as one researcher put it, and deep fakes are only going to get more and more common. They're not going away, basically, so we might as well get used to them.

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How To Make Deep Fake Video: A Guide + Warnings

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Deep fake videos are becoming increasingly popular online. Plenty of people believe they're dangerous. But how do you make a deep fake video? Before AI, we had Photoshop. And during most of the 2000s and the 2010s, this was how fake stuff was made. You edited photos to look like something else.

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How to Produce a DeepFake Video in 5 Minutes

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How to Produce a DeepFake Video in 5 Minutes

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Main idea: the main idea of the article is to show the public how to create a deepfakes in 5 minutes, basically saying that everyone can create a deepfaken and in a very short period of time.

Arguments/facts:

These are the topics of this article, How it Works, Methodology and Approach, Code Example,

dialog: What technology is required to create a deepfake

summery: Although there are some concerns about such technologies, it can have various applications and also show how easy it is nowadays to generate fake stories, raising awareness about it.

source:https://towardsdatascience.com/how-to-produce-a-deepfake-video-in-5-minutes-513984fd24b6

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In this article, we talk about a new publication (2019), part of Advances in Neural Information Processing Systems 32 (NIPS 2019), called " First Order Motion Model for Image Animation " [1]. In this paper, the authors, Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci and Nicu Sebe, present a novel way to animate a source image given a driving video, without any additional information or annotation about the object to animate.

Under the hood, they use a neural network trained to reconstruct a video, given a source frame (still image) and a latent representation of the motion in the video, which is learned during training. At test time, the model takes as input a new source image and a driving video (e.g. a sequence of frames) and predicts how the object in the source image moves according to the motion depicted in these frames.

The model tracks everything that is interesting in an animation: head movements, talking, eye tracking and even body action. For example, let us look at the GIF below: president Trump drives the cast of Game of Thrones to talk and move like him.

Before creating our own sequences, let us explore this approach a bit further. First, the training data set is a large collection of videos. During training, the authors extract frame pairs from the same video and feed them to the model. The model tries to reconstruct the video by somehow learning what are the key points in the pairs and how to represent the motion between them.

Figure 1 - Framework architecture (A. Siarohin et al., NeurIPS 2019)

To this end, the framework consists of two models: the motion estimator and the video generator. Initially, the motion estimator tries to learn a latent representation of the motion in the video. This is encoded as motion-specific key point displacements (where key points can be the position of eyes or mouth) and local affine transformations. This combination can model a larger family of transformations instead of only using the key point displacements. The output of the model is two-fold: a dense motion field and an occlusion mask. This mask defines which parts of the driving video can be reconstructed by warping the source image, and which parts should be inferred by the context because they are not present in the source image (e.g. the back of the head). For instance, consider the fashion GIF below. The back of each model is not present in the source picture, thus, it should be inferred by the model.

Next, the video generator takes as input the output of the motion detector and the source image and animates it according to the driving video; it warps that source image in ways that resemble the driving video and inpatient the parts that are occluded. Figure 1 depicts the framework architecture.

The source code of this paper is on GitHub. What I did is create a simple shell script, a thin wrapper, that utilizes the source code and can be used easily by everyone for quick experimentation.

To use it, first, you need to install the module. Run pip install deep-animator to install the library in your environment. Then, we need four items:

The model weights; of course, we do not want to train the model from scratch. Thus, we need the weights to load a pre-trained model.

A YAML configuration file for our model.

A source image; this could be for example a portrait.

A driving video; best to download a video with a clearly visible face for start.

To get some results quickly and test the performance of the algorithm you can use this source image and this driving video. The model weights can be found here. A simple YAML configuration file is given below. Open a text editor, copy and paste the following lines and save it as conf.yml.

model_params:
 common_params:
 num_kp: 10
 num_channels: 3
 estimate_jacobian: True
 kp_detector_params:
 temperature: 0.1
 block_expansion: 32
 max_features: 1024
 scale_factor: 0.25
 num_blocks: 5
 generator_params:
 block_expansion: 64
 max_features: 512
 num_down_blocks: 2
 num_bottleneck_blocks: 6
 estimate_occlusion_map: True
 dense_motion_params:
 block_expansion: 64
 max_features: 1024
 num_blocks: 5
 scale_factor: 0.25
 discriminator_params:
 scales: [1]
 block_expansion: 32
 max_features: 512
 num_blocks: 4

Now, we are ready to have a statue mimic Leonardo DiCaprio! To get your results just run the following command.

deep_animate <path_to_the_source_image> <path_to_the_driving_video> <path_to_yaml_conf> <path_to_model_weights>

For example, if you have downloaded everything in the same folder, cd to that folder and run:

deep_animate 00.png 00.mp4 conf.yml deep_animator_model.pth.tar

On my CPU, it takes around five minutes to get the generated video. This will be saved into the same folder unless specified otherwise by the --dest option. Also, you can use GPU acceleration with the --device cuda option. Finally, we are ready to see the result. Pretty awesome!

I this story, we presented the work done by A. Siarohin et al. and how to use it to obtain great results with no effort. Finally, we used deep-animator, a thin wrapper, to animate a statue.

Although there are some concerns about such technologies, it can have various applications and also show how easy it is nowadays to generate fake stories, raising awareness about it.

Learning Rate is my weekly newsletter for those who are curious about the world of AI and MLOps. You'll hear from me every Friday with updates and thoughts on the latest AI news, research, repos and books. Subscribe here!

My name is Dimitris Poulopoulos, and I'm a machine learning engineer working for Arrikto. I have designed and implemented AI and software solutions for major clients such as the European Commission, Eurostat, IMF, the European Central Bank, OECD, and IKEA.

If you are interested in reading more posts about Machine Learning, Deep Learning, Data Science, and DataOps, follow me on Medium, LinkedIn, or @james2pl on Twitter.

Opinions expressed are solely my own and do not express the views or opinions of my employer. Also, visit the resources page on my website, a place for great books and top-rated courses, to start building your own Data Science curriculum!

[1] A. Siarohin, S. Lathuilière, S. Tulyakov, E. Ricci, and N. Sebe, "First-order motion model for image animation," in Conference on Neural Information Processing Systems (NeurIPS), December 2019.

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How to Produce a DeepFake Video in 5 Minutes

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In this article, we talk about a new publication (2019), part of Advances in Neural Information Processing Systems 32 (NIPS 2019), called " First Order Motion Model for Image Animation " [1].

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Why Deepfakes Are A Net Positive For Humanity

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Why Deepfakes Are A Net Positive For Humanity

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Main idea: Deepfakes are good because it helps the humanity grow and evolve. For example, the filmaking industry can use this to do ficiton characters or actors that are deads and need to appear.

arguments/facts: Carrie Fisher more known as Leia from Star Wars died before filming Star Wars 9, so they used the deepfakes technology to film their part

dialog: How can i do a deepfake? Well, the most common way to do a deepfake for unexperienced people is using apps, for example: Zao.

summary: Deepfakes are your friend. Yes, deepfake technology has understandably become notorious in the wake of deepfake porn videos and the threat deepfakes seemingly pose to politics. However, the ability to generate realistic simulations using artificial intelligence will, on the whole, be only a positive for humanity.

source: https://www.forbes.com/sites/simonchandler/2020/03/09/why-deepfakes-are-a-net-positive-for-humanity/?sh=74e800f22f84

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Why Deepfakes Are A Net Positive For Humanity

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Deepfakes and the ability to generate realistic simulations of people using artificial intelligence will, on the whole, be only a positive for humanity.

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Main Idea: Is that deep fake it can be used to make marketing.
Arguments/facts: Deepfakes are good some times, for example in movies when an actor dies they used deepfakes to recreate the actors face to finish the movie

Dialog: It's easy to make a deepfake?

Summary: Most people are concerned about the proliferation of deepfake technology. 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.

Source: https://acortar.link/8KnqIG

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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.

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.
The Benefits of Deepfakes
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.

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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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Positive Use Cases of Synthetic Media (aka Deepfakes)

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Positive Use Cases of Synthetic Media (aka Deepfakes)

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Main idea:Deepfakes can create numerous possibilities and opportunities for all, regardless of who they are and how they interact with the world around them.

arguments/facts: Accessibility, Education,
Art, Autonomy & Expression, Public Safety & Digital reconstruction, Innovation

dialog: someone my age can create a deepfake to improve society.

summary:
Deepfakes provide an excellent opportunity to have a positive impact on our lives. AI-Generated synthetic media can be very empowering and a great enabler. Deepfakes can give people a voice, purpose, and ability to impact scale and speed. New ideas and capabilities for empowerment have emerged from all walks of life, from art, expression, and public safety to accessibility and business. Deepfakes can create possibilities for all people irrespective of their limitations by augmenting their agency.

source:https://towardsdatascience.com/positive-use-cases-of-deepfakes-49f510056387

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Technology is an excellent enabler and can be very empowering. New ideas and capabilities for empowerment have emerged because of the advancements in data science and Artificial intelligence. AI can create possibilities and opportunities for all people, regardless of who they are and how they listen, speak, or communicate.

The Book is now available at Amazon - https://www.amazon.com/Deepfakes-aka-Synthetic-Media-Humanity-ebook/dp/B0B846YCNJ/

AI-Generated Synthetic media, also known as deepfakes, have many positive use cases. AI-Generated Synthetic Media, aka Deepfakes, advances have clear benefits in certain areas, such as accessibility, education, film production, criminal forensics, and artistic expression.

In this article, I will explore some of the positive use cases of deepfakes. I firmly believe that as technology evolves and societies adjudicate the deepfake phenomenon, most of the use cases of deepfakes will be to improve people's lives and empower communities and institutions.

Technology can give people a voice, purpose, and an ability to make impact at scale and with speed.

Accessibility

Artificial intelligence can build tools to hear, see, and soon with Artificial General Intelligence (AGI), reason with increasing accuracy. AI-Generated Synthetic media can help make the accessibility tools smarter and, in some cases, even affordable and personalizable, which can help people augment their agency and gain independence. Technology, specifically AI-based tools, can bring accessible solutions to all.

Deepfake can accelerate the accessibility quest to improve equity.

Microsoft's Seeing.ai and Google's Lookout leverage AI for recognition and synthetic voice to narrate objects, people, and the world. AI-Generated synthetic media can power personalized assistive navigation apps for pedestrian travel.

Canetroller, a haptic cane controller, can help visually impaired navigate the virtual reality world with simulation and cane interactions. It can enable people with visual impairments to navigate a virtual environment by transferring their cane skills into the virtual world. The technology can help bring equity and empowerment.

For the Amyotrophic Lateral Sclerosis (ALS) patients' synthetic voice is very empowering as the horrible disease impacts the ability to speak, communicate, and motor skills. Imagine being able to talk in your own voice with your loved ones even after losing the ability to speak. Team Gleason, which provides technology, equipment, and services for people living with ALS, is working with technology companies to enable and develop AI-Generated synthetic media scenarios for people living with ALS (Lou Gehrig's Disease).

There are other use cases of Synthetic voice. VOCALiD leverages voicebank and proprietary voice blending technology to create unique vocal personas for any device that turns text into speech for those with speech and hearing difficulties. Synthetic voice is also essential to enable such patients to be independent. Deepfake voice can also help with speech impediments since birth.

Education

Deepfake technology facilitates numerous possibilities in the education domain. Schools and teachers have been using media, audio, video in the classroom for quite some time. Deepfakes can help an educator to deliver innovative lessons that are far more engaging than traditional visual and media formats.

AI-Generated synthetic media can bring historical figures back to life for a more engaging and interactive classroom. A synthetic video of reenactments or voice and video of a historical figure may have more impact, engagement, and will be a better learning tool. For example, JFK's resolution to end the cold was speech, which was never delivered, was recreated using synthetic voice with his voice and speaking style will clearly get students to learn about the issue in a creative way.

Synthetic human anatomy, sophisticated industrial machinery, and complex industrial projects can be modeled and simulated in a mixed reality world to teach students and collaborate using Microsoft Hololens.

Creative use of synthetic voice and video can increase overall success and learning outcomes with scale and limited cost.

Art

For many decades, Hollywood has used high-end CGI, VFX, and SFX technologies to create artificial but believable worlds for compelling storytelling. In the 1994's movie, Forest Gump, the protagonist meets JFK and other historical figures. The creation of the scenario and effect was accomplished using CGI and different techniques with millions of dollars. These days sophisticated CGI and VFX technologies are used in movies to generate synthetic media for telling a captivating story.

Deepfakes can democratize the costly VFX technology as a powerful tool for independent storytellers at a fraction of the cost.

Deepfakes can be a great tool to realistically realize the primary tenant of reflection, stretching, contortion, and appropriation of real events in comedy or parody. AI-Generated synthetic media can bring unprecedented opportunities in the entertainment business, and we see a lot of realization of the opportunity by independent creators or YouTube. Some great examples of movie deepfake using GANS are YouTube creators like ctrl shift face and shamook.

Cultural and entertainment businesses can use deepfakes for artistic purposes. Dalí Museum in St. Petersburg, Florida, created an exhibition called Dalí lives, bringing him back to life using deepfakes for visitors to interact and take a selfie with surrealist painter Salvador Dalí. Similarly, Samsung's AI lab in Moscow brought Mona Lisa to life by using Deepfake technology.

In the video gaming industry, AI-generated graphics and imagery can accelerate the speed of game creation. Nvidia demoed a hybrid gaming environment created by deepfakes and is working on bringing it to market soon.

Audio storytelling and book narration is another good use case of synthetic voice. The author's synthetic voice font can be used to create the author's book's audio format. Businesses can use synthetic voice-overs of the same actor in different languages to broaden the reach of their content. The creative voice technology to implement the above scenarios must be used ethically and responsibly[ 1] with a robust consent framework as it has a direct impact on the livelihood and agency of a voice artist.

Autonomy & Expression

Synthetic media can help human rights activists and journalists to remain anonymous in dictatorial and oppressive regimes. Using technology to report out atrocities on traditional or social media can be very empowering for citizen journalists and activists. Deepfake can be used to anonymize voice and faces to protect their privacy.

Deepfakes may be used to create avatar experiences for individuals online for self-expression. Personal digital avatar gives autonomy and can help individuals to extend their purpose, ideas, and belief and enable self-expression, which otherwise may be difficult for some[ 2]. Individuals suffering from certain physical or mental disabilities could use synthetic avatars of themselves for online self-expression.

Deepfakes can give individuals new tools for self-expression and integration in the online world.

Deep Empathy, a UNICEF and MIT project, utilizes deep learning to learn the characteristics of Syrian neighborhoods affected by conflict. It then simulates how cities around the world would look amid a similar conflict. The project created synthetic war-torn images of Boston, London and other key cities around the world to help increase empathy for victims of a disaster region.

There are voice technology startups that will create synthetic voice as a new kind of bereavement therapy or help people remember the deceased and connect with them.

Reach and Message Amplification

In April last year, a health charity partnered with David Beckham to produce a video and voice campaign to help end malaria. In the "malaria no more" campaign, Beckham spoke nine languages seamlessly for public appeal. The social campaign was a great example of using deepfakes to broaden the reach of a public message. Synthesia, the company behind the Beckham video, and VOCALiD, a voice startup, create tools to natively localize video and audio content for learning tools, brand marketing, audience engagement, customer service, and public messaging to broaden the reach and amplification of the message.

Text to Speech models can assist podcast creation by podcaster's voice font to create synthetic audio from text with fewer mistakes and more speed.

Deepfakes can be used by influencers to broaden their reach and increase their audience. Using deepfakes, a brand can reach many customers with highly targeted and personalized messaging. Zalando, with Cara Delevigne executed a campaign, which can become even more personal with influencer video shots and voice fonts. Lil Miquela, a CG 19-year old "taco truck expert" and social media personality, has 2.4m followers. Digital Models, AI-Generated deepfakes are becoming a new trend in fashion and brand marketing.

AI Foundation is creating personal AI for influencers and celebrities, with their consent, to engage and amplify their reach with the audience, create deeper engagement with the fans, and deliver personal experiences at scale. AI Foundation recently launched a personal digital avatar of Deepak Chopra.

Deepfakes will take influencer marketing to another level.

A few days ahead of the Delhi elections, a politician in India created a deepfake to reach out to different linguistic voters. It was the first documented use of a deepfake by a political party for campaigning purposes.

Public Safety & Digital reconstruction

Reconstructing the crime scene is a forensic science and art, using inductive and deductive reasoning and evidence. AI-Generated synthetic media can help reconstruct the scene with the interrelationship of spatial and temporal artifacts. In 2018, a team of civil investigators used cell phone videos, autopsy reports, and surveillance footage to reconstruct a virtual crime scene.

Innovation

Data and AI are helping in digital transformation and automation in many industries. Deepfake or AI-Generated Synthetic media is becoming a foundation to engage customers and provide personalized value. Reuters demonstrated a fully AI-Generated deepfake presenter-led sports news summary system to help personalize news at scale. In the fashion retail business, deepfakes can help turn customers into models by virtually trying out the latest apparel and accessories[ 3].

An exciting app will capture customers' faces, bodies, and even micro mannerisms to generate a deepfake and try out the latest fashion trends. Data Grid, a Japanese artificial intelligence company, created an artificial intelligence engine that automatically generates virtual models for advertising and fashion. The deepfake approach gives the ability for brands to have a virtual trial room for users to experience products before buying them. Retail brands can also engage customers at home by creating an AI-generated mixed reality world to try, furnish, and decorate their space.

Microsoft Hololens opens enterprise and industrial mixed reality innovative scenarios to provide value. Product Visualize lets customers explore an industrial product visually. Teams and technicians can work together in industrial products together with Remote Assist, an mixed reality collaboration tool.

AI technologies can also be used to enhance and improve the resolution of low-resolution images. These enhancing techniques to build deepfakes are super useful for older media or media created by low-resolution lenses. Google published a paper to create high-resolution images using Latent Adversarial Generator. Microsoft Research published an article to enhance and fix imperfections of the degraded photos using triplet Domain Translation Networks.

Synthetic data can empower medical researchers to develop new ways of treating diseases without actual patient data. They can use AI-Generated synthetic data for the training model to yield similar results. NVIDIA, MGH & BWH Center for Clinical Data Science and the Mayo Clinic showed how they used GANs-algorithms that iterate and improve by competing against each other-to create synthetic brain MRI images with tumors[ 4]. By training algorithms on synthetic medical images and combining them with just 10% real images, they became just as good at spotting tumors as algorithms trained only on real images.

Deepfakes may also mitigate some of the patient privacy concerns.

A vast amount of training data is required by deep learning algorithms to create an efficient model and even to develop deepfake detection models. Since there are not many real-life deepfakes, companies like Facebook had to create synthetic media and deepfakes for the Facebook Deepfake detection challenge[ 5]

Conclusion

Deepfakes provide an excellent opportunity to have a positive impact on our lives. AI-Generated synthetic media can be very empowering and a great enabler. Deepfakes can give people a voice, purpose, and ability to impact scale and speed. New ideas and capabilities for empowerment have emerged from all walks of life, from art, expression, and public safety to accessibility and business. Deepfakes can create possibilities for all people irrespective of their limitations by augmenting their agency

However, as access to synthetic media technology increases, so does the risk of exploitation. Deepfakes can be used to damage reputations, fabricate evidence, defraud the public, and undermine trust in democratic institutions.

[In the next post, I will discuss some of the malicious use cases of deepfakes].

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Technology is an excellent enabler and can be very empowering. New ideas and capabilities for empowerment have emerged because of the advancements in data science and Artificial intelligence. AI can...

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Inside the Pentagon's race against deepfake vidoes

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Inside the Pentagon's race against deepfake vidoes

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Main Idea:
Deepfakes could cause people not to trust video evidence due to deepfakes. Bottom line, deepfakes are an issue.

Arguments/facts:
Deepfakes add distrust in evidence.

This causes more conspiracy theories.

Summary:
Deepfake detection technology is being worked on and is necessary to tackle these next level fake videos.

Source: https://edition.cnn.com/interactive/2019/01/business/pentagons-race-against-deepfakes/

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Inside the Pentagon's race against deepfake videos

Advances in artificial intelligence could soon make creating convincing fake audio and video - known as "deepfakes" - relatively easy. Making a person appear to say or do something they did not has the potential to take the war of disinformation to a whole new level. Scroll down for more on deepfakes and what the US government is doing to combat them.

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Inside the Pentagon's race against deepfake videos

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Advances in artificial intelligence could soon make creating convincing fake audio and video - known as "deepfakes" - relatively easy. Making a person appear to say or do something they did not has the potential to take the war of disinformation to a whole new level.

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Main Idea: Deepfakes are bad, because they can take a famous person and put they face in a video that can damage their image
Arguments/facts:

Dialog: Deepfakes really are so bad?

Summary: The dangers of deepfakes are manipulating an audience into believing something happened that never really happened. Many experts fear that deepfakes will be used just before the November election to influence the election.

Source: https://acortar.link/8KnqIG

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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.

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.
The Benefits of Deepfakes
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.

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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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You thought fake news was bad? Deep fakes are where truth goes to die

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You thought fake news was bad? Deep fakes are where truth goes to die

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Main Idea: Deepfakes can create such big problems like DOnald Trump and Belgium. With only one deepfake, they created chaos.

Arguments/facts:
A Belgian Political Party known as sp.a posted a video on facebook and twitter. That video was fake and it showed Trump telling Belgium what to do and this made all people start insluting Trump.

Dialog: Is it Illegal? No, actually it is not Illegal

Summary: Deepfakes are really serious things. They can make someone a bad person with fake videos and that person is not bad. Basically, the life of important people can be really affected by deepfakes

Source: https://www.theguardian.com/technology/2018/nov/12/deep-fakes-fake-news-truth

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n May, a video appeared on the internet of Donald Trump offering advice to the people of Belgium on the issue of climate change. "As you know, I had the balls to withdraw from the Paris climate agreement," he said, looking directly into the camera, "and so should you."

The video was created by a Belgian political party, Socialistische Partij Anders, or sp.a,and posted on sp.a's Twitter and Facebook. It provoked hundreds of comments, many expressing outrage that the American president would dare weigh in on Belgium's climate policy.

One woman wrote: "Humpy Trump needs to look at his own country with his deranged child killers who just end up with the heaviest weapons in schools."

Another added: "Trump shouldn't blow so high from the tower because the Americans are themselves as dumb."

But this anger was misdirected. The speech, it was later revealed, was nothing more than a hi-tech forgery.

Sp.a claimed that they had commissioned a production studio to use machine learning to produce what is known as a "deep fake" - a computer-generated replication of a person, in this case Trump, saying or doing things they have never said or done.

Sp.a's intention was to use the fake video to grab people's attention, then redirect them to an online petition calling on the Belgian government to take more urgent climate action. The video's creators later said they assumed that the poor quality of the fake would be enough to alert their followers to its inauthenticity. "It is clear from the lip movements that this is not a genuine speech by Trump," a spokesperson for sp.a told Politico.

As it became clear that their practical joke had gone awry, sp.a's social media team went into damage control. "Hi Theo, this is a playful video. Trump didn't really make these statements." "Hey, Dirk, this video is supposed to be a joke. Trump didn't really say this."

The party's communications team had clearly underestimated the power of their forgery, or perhaps overestimated the judiciousness of their audience. Either way, this small, left-leaning political party had, perhaps unwittingly, provided a deeply troubling example of the use of manipulated video onlinein an explicitly political context.

It was a small-scale demonstration of how this technology might be used to threaten our already vulnerable information ecosystem - and perhaps undermine the possibility of a reliable, shared reality.

Fake videos can now be created using a machine learning technique called a "generative adversarial network", or a GAN. A graduate student, Ian Goodfellow, invented GANs in 2014 as a way to algorithmically generate new types of data out of existing data sets. For instance, a GAN can look at thousands of photos of Barack Obama, and then produce a new photo that approximates those photos without being an exact copy of any one of them, as if it has come up with an entirely new portrait of the former president not yet taken. GANs might also be used to generate new audio from existing audio, or new text from existing text - it is a multi-use technology.

The use of this machine learning technique was mostly limited to the AI research community until late 2017, when a Reddit user who went by the moniker "Deepfakes" - a portmanteau of "deep learning" and "fake" - started posting digitally altered pornographic videos. He was building GANs using TensorFlow, Google's free open source machine learning software, to superimpose celebrities' faces on the bodies of women in pornographic movies.

A number of media outlets reported on the porn videos, which became known as "deep fakes". In response, Reddit banned them for violating the site's content policy against involuntary pornography. By this stage, however, the creator of the videos had released FakeApp, an easy-to-use platform for making forged media. The free software effectively democratized the power of GANs. Suddenly, anyone with access to the internet and pictures of a person's face could generate their own deep fake.

When Danielle Citron, a professor of law at the University of Maryland, first became aware of the fake porn movies, she was initially struck by how viscerally they violated these women's right to privacy. But once she started thinking about deep fakes, she realized that if they spread beyond the trolls on Reddit they could be even more dangerous. They could be weaponized in ways that weaken the fabric of democratic society itself.

"I started thinking about my city, Baltimore," she told me. "In 2015, the place was a tinderbox after the killing of Freddie Gray. So, I started to imagine what would've happened if a deep fake emerged of the chief of police saying something deeply racist at that moment. The place would've exploded."

Citron, along with her colleague Bobby Chesney, began working on a report outlining the extent of the potential danger. As well as considering the threat to privacy and national security, both scholars became increasingly concerned that the proliferation of deep fakes could catastrophically erode trust between different factions of society in an already polarized political climate.

What would've happened if a deep fake emerged of the police chief saying something racist? The place would've exploded

Danielle Citron

In particular, they could foresee deep fakes being exploited by purveyors of "fake news". Anyone with access to this technology - from state-sanctioned propagandists to trolls - would be able to skew information, manipulate beliefs, and in so doing, push ideologically opposed online communities deeper into their own subjective realities.

"The marketplace of ideas already suffers from truth decay as our networked information environment interacts in toxic ways with our cognitive biases," the report reads. "Deep fakes will exacerbate this problem significantly."

Citron and Chesney are not alone in these fears. In April, the film director Jordan Peele and BuzzFeed released a deep fake of Barack Obama calling Trump a "total and complete dipshit" to raise awareness about how AI-generated synthetic media might be used to distort and manipulate reality. In September, three members of Congress sent a letter to the director of national intelligence, raising the alarm about how deep fakes could be harnessed by "disinformation campaigns in our elections".

The specter of politically motivated deep fakes disrupting elections is at the top of Citron's concerns. "What keeps me awake at night is a hypothetical scenario where, before the vote in Texas, someone releases a deep fake of Beto O'Rourke having sex with a prostitute, or something," Citron told me. "Now, I know that this would be easily refutable, but if this drops the night before, you can't debunk it before serious damage has spread."

She added: "I'm starting to see how a well-timed deep fake could very well disrupt the democratic process."

While these disturbing hypotheticals might be easy to conjure, Tim Hwang, director of the Harvard-MIT Ethics and Governance of Artificial Intelligence Initiative, is not willing to bet on deep fakes having a high impact on elections in the near future. Hwang has been studying the spread of misinformation on online networks for a number of years, and, with the exception of the small-stakes Belgian incident, he is yet to see any examples of truly corrosive incidents of deep fakes "in the wild".

Hwang believes that that this is partly because using machine learning to generate convincing fake videos still requires a degree of expertise and lots of data. "If you are a propagandist, you want to spread your work as far as possible with the least amount of effort," he said. "Right now, a crude Photoshop job could be just as effective as something created with machine learning."

At the same time, Hwang acknowledges that as deep fakes become more realistic and easier to produce in the coming years, they could usher in an era of forgery qualitatively different from what we have seen before.

"We have long been able to doctor images and movies," he said. "But in the past, if you wanted to make a video of the president saying something he didn't say, you needed a team of experts. Machine learning will not only automate this process, it will also probably make better forgeries."

Couple this with the fact that access to this technology will spread over the internet, and suddenly you have, as Hwang put it, "a perfect storm of misinformation".

Nonetheless, research into machine learning-powered synthetic media forges ahead.

In August, an international team of researchers affiliated with Germany's Max Planck Institute for Informatics unveiled a technique for producing what they called "deep video portraits", a sort of facial ventriloquism, where one person can take control of another person's face and make it say or do things at will. A video accompanying the research paper depicted a researcher opening his mouth and a corresponding moving image of Barack Obama opening his mouth; the researcher then moves his head to the side, and so does synthetic Obama.

Christian Theobalt, a researcher involved in the study, told me via email that he imagines deep video portraits will be used most effectively for accurate dubbing in foreign films, advanced face editing techniques for post-production in film, and special effects. In a press release that accompanied the original paper, the researchers acknowledged potential misuse of their technology, but emphasized how their approach - capable of synthesizing faces that look "nearly indistinguishable from ground truth" - could make "a real difference to the visual entertainment industry".

Hany Farid, professor of computer science at the University of California, Berkeley, believes that although the machine learning-powered breakthroughs in computer graphics are impressive, researchers should be more cognizant of the broader social and political ramifications of what they're creating. "The special effects community will love these new technologies," Farid told me. "But outside of this world, outside of Hollywood, it is not clear to me that the positive implications outweigh the negative."

Farid, who has spent the past 20 years developing forensic technology to identify digital forgeries, is currently working on new detection methods to counteract the spread of deep fakes. One of Farid's recent breakthroughs has been focusing on subtle changes of color that occur in the face as blood is pumped in and out. The signal is so minute that the machine learning software is unable to pick it up - at least for now.

As the threat of deep fakes intensifies, so do efforts to produce new detection methods. In June, researchers from the University at Albany (SUNY) published a paper outlining how fake videos could be identified by a lack of blinking in synthetic subjects. Facebook has also committed to developing machine learning models to detect deep fakes.

But Farid is wary. Relying on forensic detection alone to combat deep fakes is becoming less viable, he believes, due to the rate at which machine learning techniques can circumvent them. "It used to be that we'd have a couple of years between coming up with a detection technique and the forgers working around it. Now it only takes two to three months."

This, he explains, is due to the flexibility of machine learning. "All the programmer has to do is update the algorithm to look for, say, changes of color in the face that correspond with the heartbeat, and then suddenly, the fakes incorporate this once imperceptible sign." (For this reason, Farid chose not to share some of his more recent forensic breakthroughs with me. "Once I spill on the research, all it takes is one asshole to add it to their system.")

Although Farid is locked in this technical cat-and-mouse game with deep fake creators, he is aware that the solution does not lie in new technology alone. "The problem isn't just that deep fake technology is getting better," he said. "It is that the social processes by which we collectively come to know things and hold them to be true or untrue are under threat."

Indeed, as the fake video of Trump that spread through social networks in Belgium earlier this year demonstrated, deep fakes don't need to be undetectable or even convincing to be believed and do damage. It is possible that the greatest threat posed by deep fakes lies not in the fake content itself, but in the mere possibility of their existence.

This is a phenomenon that scholar Aviv Ovadya has called "reality apathy", whereby constant contact with misinformation compels people to stop trusting what they see and hear. In other words, the greatest threat isn't that people will be deceived, but that they will come to regard everything as deception.

Recent polls indicate that trust in major institutions and the media is dropping. The proliferation of deep fakes, Ovadya says, is likely to exacerbate this trend.

According to Danielle Citron, we are already beginning to see the social ramifications of this epistemic decay.

"Ultimately, deep fakes are simply amplifying what I call the liar's dividend," she said. "When nothing is true then the dishonest person will thrive by saying what's true is fake."

This article has been amended to clarify that though sp.a initially claimed it used machine learning technology to create its fake Trump clip, it was later revealed that the video was made using After Effects, an editing software

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You thought fake news was bad? Deep fakes are where truth goes to die

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Technology can make it look as if anyone has said or done anything. Is it the next wave of (mis)information warfare?

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Deepfakes: A threat to democracy or just a bit of fun?

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Deepfakes: A threat to democracy or just a bit of fun?

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Main Idea: That is scary, that we are already at the point where you can't tell the difference between deepfakes and the real thing.

Arguments/facts: One argument is that all, or the majority of deepfakes are of famous people.

Dialog: Is reali so scary as many people say?

Summary: Like other deepfake tools, his software creates computer-manipulated videos of people, often politicians or celebrities, that are designed to look real.
Most often this involves face swapping, whereby the face of a celebrity is overlaid onto the likeness of someone else.

Source: https://www.bbc.com/news/business-51204954

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By Daniel Thomas
Business reporter, BBC News, Davos

Media caption,
WATCH: This deepfake software can map celebrity faces on to anyone

"We are already at the point where you can't tell the difference between deepfakes and the real thing," Professor Hao Li of the University of Southern California tells the BBC.

We are at the computer scientist's deepfake installation at the World Economic Forum in Davos which gives a hint of what he means.

Like other deepfake tools, his software creates computer-manipulated videos of people - often politicians or celebrities - that are designed to look real.

Most often this involves "face swapping", whereby the face of a celebrity is overlaid onto the likeness of someone else.

As I sit, a camera films my face and projects it onto a screen in front of me; my features are then digitally mapped.

Image caption,
Prof Hao Li says deepfakes are 'dangerous'

One after the other the faces of actor Leonardo DiCaprio, former UK Prime Minister Theresa May and footballer Lionel Messi are superimposed onto the image of my own face in real time - their features and expressions merging seamlessly with mine.

The effects, while impressive, are more comical than sinister but feasibly could confuse some viewers. However, when the professor shows me another deepfake video he has been working on which is yet to be unveiled to the public, I totally understand what he means.

It shows a famous world leader giving a speech and is impossible to distinguish from the real thing.

"Just think of the potential for misuse and disinformation we could see with this type of thing," says Prof Li.

Deepfakes only hit the headlines in 2017 after crudely produced videos began to surface on the internet, typically involving celebrity face swapping or manipulation.

Image source, Hao Li

Image caption,
Alec Baldwin impersonating President Trump: The real Alec Baldwin is on the left - could you tell?

Some were send-ups of well-known figures, voiced by impressionists or comedians. But in the vast majority of cases, famous people's faces were superimposed onto those of porn stars, much to the alarm of those targeted.

Since then the technology - which relies on complex machine learning algorithms - has evolved rapidly and deepfakes have become more common. Some have been used as "fake news". Others have been cited in cases of online fraud. Facebook has even banned them from its platform for fear they could be used to manipulate people.

Prof Li's own software was never designed to trick people and will be sold exclusively to businesses, he says. But he thinks a dangerous genie could be about to escape its bottle as deepfake technology falls into the wrong hands - and democracy is at threat.

"The first risk is that people are already using the fact deepfakes exist to discredit genuine video evidence. Even though there's footage of you doing or saying something you can say it was a deepfake and it's very hard to prove otherwise."

Image source, Getty Images

Image caption,
Deepfake tech uses thousands of still images of the person

Politicians around the world have already been accused of using this ploy, one being Joao Doria, the governor of Sao Paulo in Brazil. In 2018 the married politician claimed a video allegedly showing him at an orgy was a deepfake - and no one has been able to prove conclusively that it wasn't.

However, the greater threat is the potential for deepfakes to be used in political disinformation campaigns, says Prof Li. "Elections are already being manipulated with fake news, so imagine what would happen if you added sophisticated deepfakes to the mix?"

So far clips such as the one of Ms Pelosi are not hard to spot as fakes. But done subtly, he says, people could put start to words into the mouths of politicians and no one would know - or at least by the time it was corrected it would be too late.

"It could be even more dangerous in developing countries where digital literacy is more limited. There you could really impact how society would react. You could even spread stuff that got people killed."

But some, like the Dutch cyber security firm Deeptrace, which tracks the technology, feel the panic over deepfakes has been overblown.

Director Giorgio Patrini says it is relatively easy to pull off a convincing deepfake when the person being mimicked is someone you don't know. But if they are a politician or celebrity familiar to millions it's much harder. "People are just too familiar with their voices and facial expressions," he tells the BBC.

"You would also need to be able to impersonate their voice and make them say things they would credibly say, which limits what you can do."

'Dark day'

In addition, while he accepts the most sophisticated - and dangerous - deepfake tools are freely available in open source on the internet, he says they still require expertise to use. "That's not to say they won't become more widely commodified and accessible, but I don't think it'll happen so quickly. I think it will take years."

Nevertheless, Mr Patrini thinks indistinguishable deepfakes will become commonplace eventually and could be used as a serious political weapon.

Offering a taste of what this might look like, Facebook in December removed a network of more than 900 fake accounts from its platforms that allegedly used deceptive practices to push right wing ideology online.

Notably, the accounts had used fake profile photos of fake faces generated using artificial intelligence.

Both Prof Li and Deeptrace have created deepfake detection tools, although they admit cyber criminals will work tirelessly try to get around them.

However, Mr Patrini is optimistic: "Even when deepfakes are so sophisticated humans cannot tell the difference, I believe we will be able to build more sophisticated tools to spot them. It's like anti-virus software - it will keep being updated and improved."

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Deepfakes: A threat to democracy or just a bit of fun?

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Deepfakes, or computer-generated images of people, can be dangerous for democracy, warn experts.

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Main idea:

Arguments/facts: What is a deepfake?, Educational deepfake examples, Educational deepfake examples, Reaching worldwide, audiences, Deepfake characters, Positive deepfake examples in the art world, Deepfake examples in medicine, Training, Earning trust, proceed with caution.

dialog:

summary:Artificial intelligence technology, to many, is scary. It’s packed full of ethical questions and uncertain future applications. Perhaps one of the more alarming functionalities enabled by AI advancement is the creation of deepfakes.

source: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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What are deepfakes – and how can you spot them?

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What are deepfakes – and how can you spot them?

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Main Idea: deepfakes use a form of artificial intelligence called deep learning to make images of fake events, hence the name deepfake.

Arguments/facts: 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

Dialog: Why do deepfakes exist? Deepfake technology is used in synthetic media to create falsified content, replacing or synthesizing faces, speech, and manipulating emotions. It is used to digitally imitate an action by a person that he or she did not commit

Summary: Deepfakes exist, they are real and we have to able to spot them. They can be used for good things but normally are to ridiculized or put someone into a problem. We should stop doing deepfakes for a bad use.

Source: 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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Main Idea:

Deepfakes are AI generated video where a face is transfered to a different subject's face.

Arguments/facts:

96% of deepfakes are pornographic

Deepfakes started in the year 1990

Takes hours of footage to get a realistic deepfake

Microsoft is working on Deepfake detection technology

Summary:

Deepfakes are super realistic and can cause some real issues, but they also can be used for good. As well people are working on programs to validate the authenticity of footage.

Source:
https://en.wikipedia.org/wiki/Deepfake

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Deepfakes (a portmanteau of " deep learning" and "fake") are synthetic media in which a person in an existing image or video is replaced with someone else's likeness. While the act of creating fake content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content that can more easily deceive. The main machine learning methods used to create deepfakes are based on deep learning and involve training generative neural network architectures, such as autoencoders, or generative adversarial networks (GANs).

Deepfakes have garnered widespread attention for their potential use in creating child sexual abuse material, celebrity pornographic videos, revenge porn, fake news, hoaxes, bullying, and financial fraud. This has elicited responses from both industry and government to detect and limit their use.

From traditional entertainment to gaming, deepfake technology has evolved to be increasingly convincing and available to the public, allowing the disruption of the entertainment and media industries.

Photo manipulation was developed in the 19th century and soon applied to motion pictures. Technology steadily improved during the 20th century, and more quickly with the advent of digital video.

Deepfake technology has been developed by researchers at academic institutions beginning in the 1990s, and later by amateurs in online communities. More recently the methods have been adopted by industry.

Academic research related to deepfakes is split between the field of computer vision, a subfield of computer science, which develops techniques for creating and identifying deepfakes, and humanities and social science approaches that study the social, ethical and aesthetic implications of deepfakes.

In cinema studies, deepfakes demonstrate how "the human face is emerging as a central object of ambivalence in the digital age". Video artists have used deepfakes to "playfully rewrite film history by retrofitting canonical cinema with new star performers". Film scholar Christopher Holliday analyses how the switching out gender and race of performers in familiar movie scenes destabilise gender classifications and categories. The idea of " queering" deepfakes is also discussed in Oliver M. Gingrich's discussion of media artworks that use deepfakes to reframe gender, including British artist Jake Elwes' Zizi: Queering the Dataset, an artwork that uses deepfakes of drag queens to intentionally play with gender. The aesthetic potentials of deepfakes are also beginning to be explored. Theatre historian John Fletcher notes that early demonstrations of deepfakes are presented as performances, and situates these in the context of theatre, discussing "some of the more troubling paradigm shifts" that deepfakes represent as a performance genre.

Philosophers and media scholars have discussed the ethics of deepfakes especially in relation to pornography. Media scholar Emily van der Nagel draws upon research in photography studies on manipulated images to discuss verification systems that allow women to consent to uses of their images.

Beyond pornography, deepfakes have been framed by philosophers as an "epistemic threat" to knowledge and thus to society. There are several other suggestions for how to deal with the risks deepfakes give rise beyond pornography, but also to corporations, politicians and others, of "exploitation, intimidation, and personal sabotage", and there are several scholarly discussions of potential legal and regulatory responses both in legal studies and media studies. In psychology and media studies, scholars discuss the effects of disinformation that uses deepfakes, and the social impact of deepfakes.

While most English-language academic studies of deepfakes focus on the Western anxieties about disinformation and pornography, digital anthropologist Gabriele de Seta has analysed the Chinese reception of deepfakes, which are known as huanlian, which translates to "changing faces". The Chinese term does not contain the "fake" of the English deepfake, and de Seta argues that this cultural context may explain why the Chinese response has been more about practical regulatory responses to "fraud risks, image rights, economic profit, and ethical imbalances".

An early landmark project was the Video Rewrite program, published in 1997, which modified existing video footage of a person speaking to depict that person mouthing the words contained in a different audio track. It was the first system to fully automate this kind of facial reanimation, and it did so using machine learning techniques to make connections between the sounds produced by a video's subject and the shape of the subject's face.

Contemporary academic projects have focused on creating more realistic videos and on improving techniques. The "Synthesizing Obama" program, published in 2017, modifies video footage of former president Barack Obama to depict him mouthing the words contained in a separate audio track. The project lists as a main research contribution its photorealistic technique for synthesizing mouth shapes from audio. The Face2Face program, published in 2016, modifies video footage of a person's face to depict them mimicking the facial expressions of another person in real time. The project lists as a main research contribution the first method for re-enacting facial expressions in real time using a camera that does not capture depth, making it possible for the technique to be performed using common consumer cameras.

In August 2018, researchers at the University of California, Berkeley published a paper introducing a fake dancing app that can create the impression of masterful dancing ability using AI. This project expands the application of deepfakes to the entire body; previous works focused on the head or parts of the face.

Researchers have also shown that deepfakes are expanding into other domains such as tampering with medical imagery. In this work, it was shown how an attacker can automatically inject or remove lung cancer in a patient's 3D CT scan. The result was so convincing that it fooled three radiologists and a state-of-the-art lung cancer detection AI. To demonstrate the threat, the authors successfully performed the attack on a hospital in a White hat penetration test.

A survey of deepfakes, published in May 2020, provides a timeline of how the creation and detection deepfakes have advanced over the last few years. The survey identifies that researchers have been focusing on resolving the following challenges of deepfake creation:

Generalization. High-quality deepfakes are often achieved by training on hours of footage of the target. This challenge is to minimize the amount of training data and the time to train the model required to produce quality images and to enable the execution of trained models on new identities (unseen during training).

Paired Training. Training a supervised model can produce high-quality results, but requires data pairing. This is the process of finding examples of inputs and their desired outputs for the model to learn from. Data pairing is laborious and impractical when training on multiple identities and facial behaviors. Some solutions include self-supervised training (using frames from the same video), the use of unpaired networks such as Cycle-GAN, or the manipulation of network embeddings.

Identity leakage. This is where the identity of the driver (i.e., the actor controlling the face in a reenactment) is partially transferred to the generated face. Some solutions proposed include attention mechanisms, few-shot learning, disentanglement, boundary conversions, and skip connections.

Occlusions. When part of the face is obstructed with a hand, hair, glasses, or any other item then artifacts can occur. A common occlusion is a closed mouth which hides the inside of the mouth and the teeth. Some solutions include image segmentation during training and in-painting.

Temporal coherence. In videos containing deepfakes, artifacts such as flickering and jitter can occur because the network has no context of the preceding frames. Some researchers provide this context or use novel temporal coherence losses to help improve realism. As the technology improves, the interference is diminishing.

Overall, deepfakes are expected to have several implications in media and society, media production, media representations, media audiences, gender, law, and regulation, and politics.

The term deepfakes originated around the end of 2017 from a Reddit user named "deepfakes". He, as well as others in the Reddit community r/deepfakes, shared deepfakes they created; many videos involved celebrities' faces swapped onto the bodies of actresses in pornographic videos, while non-pornographic content included many videos with actor Nicolas Cage's face swapped into various movies.

Other online communities remain, including Reddit communities that do not share pornography, such as r/SFWdeepfakes (short for "safe for work deepfakes"), in which community members share deepfakes depicting celebrities, politicians, and others in non-pornographic scenarios. Other online communities continue to share pornography on platforms that have not banned deepfake pornography.

In January 2018, a proprietary desktop application called FakeApp was launched. This app allows users to easily create and share videos with their faces swapped with each other. As of 2019, FakeApp has been superseded by open-source alternatives such as Faceswap, command line-based DeepFaceLab, and web-based apps such as DeepfakesWeb.com

Larger companies started to use deepfakes. Corporate training videos can be created using deepfaked avatars and their voices, for example Synthesia, which uses deepfake technology with avatars to create personalized videos. The mobile app giant Momo created the application Zao which allows users to superimpose their face on television and movie clips with a single picture. As of 2019 the Japanese AI company DataGrid made a full body deepfake that could create a person from scratch. They intend to use these for fashion and apparel.

As of 2020 audio deepfakes, and AI software capable of detecting deepfakes and cloning human voices after 5 seconds of listening time also exist. A mobile deepfake app, Impressions, was launched in March 2020. It was the first app for the creation of celebrity deepfake videos from mobile phones.

Deepfakes technology can not only be used to fabricate messages and actions of others, but it can also be used to revive deceased individuals. On 29 October 2020, Kim Kardashian posted a video of her late father Robert Kardashian; the face in the video of Robert Kardashian was created with deepfake technology. This hologram was created by the company Kaleida, where they use a combination of performance, motion tracking, SFX, VFX and DeepFake technologies in their hologram creation.

In 2020, Joaquin Oliver, victim of the Parkland shooting was resurrected with deepfake technology. Oliver's parents teamed up on behalf of their organization Nonprofit Change the Ref, with McCann Health to produce this deepfake video advocating for gun-safety voting campaign. In this deepfake message, it shows Joaquin encouraging viewers to vote.

In 2022, Elvis Presley has been resurrected in America's Got Talent 17 using deepfake technology.

Deepfakes rely on a type of neural network called an autoencoder. These consist of an encoder, which reduces an image to a lower dimensional latent space, and a decoder, which reconstructs the image from the latent representation. Deepfakes utilize this architecture by having a universal encoder which encodes a person in to the latent space. The latent representation contains key features about their facial features and body posture. This can then be decoded with a model trained specifically for the target. This means the target's detailed information will be superimposed on the underlying facial and body features of the original video, represented in the latent space.

A popular upgrade to this architecture attaches a generative adversarial network to the decoder. A GAN trains a generator, in this case the decoder, and a discriminator in an adversarial relationship. The generator creates new images from the latent representation of the source material, while the discriminator attempts to determine whether or not the image is generated. This causes the generator to create images that mimic reality extremely well as any defects would be caught by the discriminator. Both algorithms improve constantly in a zero sum game. This makes deepfakes difficult to combat as they are constantly evolving; any time a defect is determined, it can be corrected.

Deepfakes can be used to generate blackmail materials that falsely incriminate a victim. A report by the American Congressional Research Service warned that deepfakes could be used to blackmail elected officials or those with access to classified information for espionage or influence purposes.

Alternatively, since the fakes cannot reliably be distinguished from genuine materials, victims of actual blackmail can now claim that the true artifacts are fakes, granting them plausible deniability. The effect is to void credibility of existing blackmail materials, which erases loyalty to blackmailers and destroys the blackmailer's control. This phenomenon can be termed "blackmail inflation", since it "devalues" real blackmail, rendering it worthless. It is possible to utilize commodity GPU hardware with a small software program to generate this blackmail content for any number of subjects in huge quantities, driving up the supply of fake blackmail content limitlessly and in highly scalable fashion.

In 2017, Deepfake pornography prominently surfaced on the Internet, particularly on Reddit. As of 2019, many deepfakes on the internet feature pornography of female celebrities whose likeness is typically used without their consent. A report published in October 2019 by Dutch cybersecurity startup Deeptrace estimated that 96% of all deepfakes online were pornographic. As of 2018, a Daisy Ridley deepfake first captured attention, among others. As of October 2019, most of the deepfake subjects on the internet were British and American actresses. However, around a quarter of the subjects are South Korean, the majority of which are K-pop stars.

In June 2019, a downloadable Windows and Linux application called DeepNude was released that used neural networks, specifically generative adversarial networks, to remove clothing from images of women. The app had both a paid and unpaid version, the paid version costing $50. On 27 June the creators removed the application and refunded consumers.

Deepfakes have been used to misrepresent well-known politicians in videos.

In April 2018, Jordan Peele collaborated with Buzzfeed to create a deepfake of Barack Obama with Peele's voice; it served as a public service announcement to increase awareness of deepfakes.

In 2018, in separate videos, the face of the Argentine President Mauricio Macri had been replaced by the face of Adolf Hitler, and Angela Merkel's face has been replaced with Donald Trump's.

In January 2019, Fox affiliate KCPQ aired a deepfake of Trump during his Oval Office address, mocking his appearance and skin color. The employee found responsible for the video was subsequently fired.

In June 2019, the United States House Intelligence Committee held hearings on the potential malicious use of deepfakes to sway elections.

In April 2020, the Belgian branch of Extinction Rebellion published a deepfake video of Belgian Prime Minister Sophie Wilmès on Facebook. The video promoted a possible link between deforestation and COVID-19. It had more than 100,000 views within 24 hours and received many comments. On the Facebook page where the video appeared, many users interpreted the deepfake video as genuine.

During the 2020 US Presidential campaign, many deep fakes surfaced purporting Joe Biden in cognitive decline-falling asleep during an interview, getting lost, and misspeaking-all bolstering rumors of his decline.

During the 2020 Delhi Legislative Assembly election campaign, the Delhi Bharatiya Janata Party used similar technology to distribute a version of an English-language campaign advertisement by its leader, Manoj Tiwari, translated into Haryanvi to target Haryana voters. A voiceover was provided by an actor, and AI trained using video of Tiwari speeches was used to lip-sync the video to the new voiceover. A party staff member described it as a "positive" use of deepfake technology, which allowed them to "convincingly approach the target audience even if the candidate didn't speak the language of the voter."

In 2020, Bruno Sartori produced deepfakes parodying politicians like Jair Bolsonaro and Donald Trump.

In April 2021, politicians in a number of European countries were approached by pranksters Vovan and Lexus, who are accused by critics of working for the Russian state. They impersonated Leonid Volkov, a Russian opposition politician and chief of staff of the Russian opposition leader Alexei Navalny's campaign, allegedly through deepfake technology. However, the pair told that they did not use deepfakes, and just used a look-alike.

In March 2018 the multidisciplinary artist Joseph Ayerle published the video artwork Un'emozione per sempre 2.0 (English title: The Italian Game). The artist worked with Deepfake technology to create an AI actress, a synthetic version of 80s movie star Ornella Muti, traveling in time from 1978 to 2018. The Massachusetts Institute of Technology referred this artwork in the study "Collective Wisdom". The artist used Ornella Muti's time travel to explore generational reflections, while also investigating questions about the role of provocation in the world of art. For the technical realization Ayerle used scenes of photo model Kendall Jenner. The program replaced Jenner's face by an AI calculated face of Ornella Muti. As a result, the AI actress has the face of the Italian actress Ornella Muti and the body of Kendall Jenner.

Deepfakes have been widely used in satire or to parody celebrities and politicians. The 2020 webseries Sassy Justice, created by Trey Parker and Matt Stone, heavily features the use of deepfaked public figures to satirize current events and raise awareness of deepfake technology.

There has been speculation about deepfakes being used for creating digital actors for future films. Digitally constructed/altered humans have already been used in films before, and deepfakes could contribute new developments in the near future. Deepfake technology has already been used by fans to insert faces into existing films, such as the insertion of Harrison Ford's young face onto Han Solo's face in Solo: A Star Wars Story, and techniques similar to those used by deepfakes were used for the acting of Princess Leia and Grand Moff Tarkin in

As deepfake technology increasingly advances, Disney has improved their visual effects using high-resolution deepfake face swapping technology. Disney improved their technology through progressive training programmed to identify facial expressions, implementing a face-swapping feature, and iterating in order to stabilize and refine the output. This high-resolution deepfake technology saves significant operational and production costs. Disney's deepfake generation model can produce AI-generated media at a 1024 x 1024 resolution, as opposed to common models that produce media at a 256 x 256 resolution. The technology allows Disney to characters or revive deceased actors.

The 2020 documentary Welcome to Chechnya used deepfake technology to obscure the identity of the people interviewed, so as to protect them from retaliation.

On June 8, 2022, Daniel Emmet, a former AGT contestant, teamed up with the AI startup Metaphysic AI, to create a hyperrealistic deepfake to make it appear as Simon Cowell. Cowell, notoriously known for severely critiquing contestants, was on stage interpreting " You're The Inspiration" by Chicago. Emmet sang on stage as an image of Simon Cowell emerged on the screen behind him in flawless synchronicity.

On August 30, 2022, Metaphysic AI had 'deep-fake' Simon Cowell, Howie Mandel and Terry Crews singing opera on stage.

On September 13, 2022, Metaphysic AI performed with a synthetic version of Elvis Presley for the finals of America's Got Talent.

The MIT artificial intelligence project 15.ai has been used for content creation for multiple Internet fandoms, particularly on social media.

In 2020, an internet meme emerged utilizing deepfakes to generate videos of people singing the chorus of "Baka Mitai", a song from the game in the video game series . In the series, the melancholic song is sung by the player in a karaoke minigame. Most iterations of this meme use a 2017 video uploaded by user Dobbsyrules, who lip syncs the song, as a template.

Deepfakes have begun to see use in popular social media platforms, notably through Zao, a Chinese deepfake app that allows users to substitute their own faces onto those of characters in scenes from films and television shows such as Romeo + Juliet and Game of Thrones. The app originally faced scrutiny over its invasive user data and privacy policy, after which the company put out a statement claiming it would revise the policy. In January 2020 Facebook announced that it was introducing new measures to counter this on its platforms.

The Congressional Research Service cited unspecified evidence as showing that foreign intelligence operatives used deepfakes to create social media accounts with the purposes of recruiting individuals with access to classified information.

In 2021, realistic deepfake videos of actor Tom Cruise were released on TikTok, which went viral and garnered more than tens of millions of views. The deepfake videos featured an "artificial intelligence-generated doppelganger" of Cruise doing various activities such as teeing off at the golf course, showing off a coin trick, and biting into a lollipop. The creator of the clips, Belgian VFX Artist Chris Umé, said he first got interested in deepfakes in 2018 and saw the "creative potential" of them.

Deepfake photographs can be used to create sockpuppets, non-existent people, who are active both online and in traditional media. A deepfake photograph appears to have been generated together with a legend for an apparently non-existent person named Oliver Taylor, whose identity was described as a university student in the United Kingdom. The Oliver Taylor persona submitted opinion pieces in several newspapers and was active in online media attacking a British legal academic and his wife, as "terrorist sympathizers." The academic had drawn international attention in 2018 when he commenced a lawsuit in Israel against NSO, a surveillance company, on behalf of people in Mexico who alleged they were victims of NSO's phone hacking technology. Reuters could find only scant records for Oliver Taylor and "his" university had no records for him. Many experts agreed that the profile photo is a deepfake. Several newspapers have not retracted articles attributed to him or removed them from their websites. It is feared that such techniques are a new battleground in disinformation.

Collections of deepfake photographs of non-existent people on social networks have also been deployed as part of Israeli partisan propaganda. The Facebook page "Zionist Spring" featured photos of non-existent persons along with their "testimonies" purporting to explain why they have abandoned their left-leaning politics to embrace the right-wing, and the page also contained large numbers of posts from Prime Minister of Israel Benjamin Netanyahu and his son and from other Israeli right wing sources. The photographs appear to have been generated by " human image synthesis" technology, computer software that takes data from photos of real people to produce a realistic composite image of a non-existent person. In much of the "testimony," the reason given for embracing the political right was the shock of learning of alleged incitement to violence against the prime minister. Right wing Israeli television broadcasters then broadcast the "testimony" of these non-existent person based on the fact that they were being "shared" online. The broadcasters aired the story, even though the broadcasters could not find such people, explaining "Why does the origin matter?" Other Facebook fake profiles-profiles of fictitious individuals-contained material that allegedly contained such incitement against the right wing prime minister, in response to which the prime minister complained that there was a plot to murder him.

Audio deepfakes have been used as part of social engineering scams, fooling people into thinking they are receiving instructions from a trusted individual. In 2019, a U.K.-based energy firm's CEO was scammed over the phone when he was ordered to transfer €220,000 into a Hungarian bank account by an individual who used audio deepfake technology to impersonate the voice of the firm's parent company's chief executive.

Though fake photos have long been plentiful, faking motion pictures has been more difficult, and the presence of deepfakes increases the difficulty of classifying videos as genuine or not. AI researcher Alex Champandard has said people should know how fast things can be corrupted with deepfake technology, and that the problem is not a technical one, but rather one to be solved by trust in information and journalism. Deepfakes can be leveraged to defame, impersonate, and spread disinformation. The primary pitfall is that humanity could fall into an age in which it can no longer be determined whether a medium's content corresponds to the truth.

Similarly, computer science associate professor Hao Li of the University of Southern California states that deepfakes created for malicious use, such as fake news, will be even more harmful if nothing is done to spread awareness of deepfake technology. Li predicted that genuine videos and deepfakes would become indistinguishable in as soon as half a year, as of October 2019, due to rapid advancement in artificial intelligence and computer graphics.

Former Google fraud czar Shuman Ghosemajumder has called deepfakes an area of "societal concern" and said that they will inevitably evolve to a point at which they can be generated automatically, and an individual could use that technology to produce millions of deepfake videos.

The consequences of a deepfake are not significant enough to destabilize the entire government system; however, deepfakes possess the ability to damage individual entities tremendously. This is because deepfakes are often targeted at one individual, and/or their relations to others in hopes to create a narrative powerful enough to influence public opinion or beliefs. This can be done through deepfake voice phishing, which manipulates audio to create fake phone calls or conversations. Another method of deepfake use is fabricated private remarks, which manipulate media to convey individuals voicing damaging comments.

In September 2020 Microsoft made public that they are developing a Deepfake detection software tool.

On April 17, 2018, American actor Jordan Peele, BuzzFeed, and Monkeypaw Productions posted a deepfake of Barack Obama to YouTube, which depicted Barack Obama cursing and calling Donald Trump names. In this deepfake Peele's voice and face were transformed and manipulated into those of Obama. The intent of this video was to portray the dangerous consequences and power of deepfakes, and how deepfakes can make anyone say anything.

On May 5, 2019, Derpfakes posted a deepfake of Donald Trump to YouTube, based on a skit Jimmy Fallon performed on NBC's The Tonight Show. In the original skit (aired May 4, 2016), Jimmy Fallon dressed as Donald Trump and pretended to participate in a phone call with Barack Obama, conversing in a manner that presented him to be bragging about his primary win in Indiana. In the deepfake, Jimmy Fallon's face was transformed into Donald Trump's face, with the audio remaining the same. This deepfake video was produced by Derpfakes with a comedic intent.

Nancy Pelosi

In 2019, a clip from Nancy Pelosi's speech at the Center for American Progress (given on May 22, 2019) in which the video was slowed down, in addition to the pitch of the audio being altered, to make it seem as if she were drunk, was widely distributed on social media. Critics argue that this was not a deepfake, but a shallowfake; a less sophisticated form of video manipulation.

On September 29, 2020, deepfakes of North Korean leader Kim Jong-un and Russian President Vladimir Putin were uploaded to YouTube, created by a nonpartisan advocacy group RepresentUs.

The deepfakes of Kim and Putin were meant to air publicly as commercials to relay the notion that interference by these leaders in US elections would be detrimental to the United States' democracy. The commercials also aimed to shock Americans to realize how fragile democracy is, and how media and news can significantly influence the country's path regardless of credibility. However, while the commercials included an ending comment detailing that the footage was not real, they ultimately did not air due to fears and sensitivity regarding how Americans may react.

On March 16, 2022, a one-minute long deepfake video depicting Ukraine's president Volodymyr Zelenskyy seemingly telling his soldiers to lay down their arms and surrender during the 2022 Russian invasion of Ukraine was circulated on social media. Russian social media boosted it, but after it was debunked, Facebook and YouTube removed it. Twitter allowed the video in tweets where it was exposed as a fake, but said it would be taken down if posted to deceive people. Hackers inserted the disinformation into a live scrolling-text news crawl on TV station Ukraine 24, and the video appeared briefly on the station's website in addition to false claims that Zelenskyy had fled his country's capital, Kyiv. It was not immediately clear who created the deepfake, to which Zelenskyy responded with his own video, saying, "We don't plan to lay down any arms. Until our victory."

Twitter is taking active measures to handle synthetic and manipulated media on their platform. In order to prevent disinformation from spreading, Twitter is placing a notice on tweets that contain manipulated media and/or deepfakes that signal to viewers that the media is manipulated. There will also be a warning that appears to users who plan on retweeting, liking, or engaging with the tweet. Twitter will also work to provide users a link next to the tweet containing manipulated or synthetic media that links to a Twitter Moment or credible news article on the related topic-as a debunking action. Twitter also has the ability to remove any tweets containing deepfakes or manipulated media that may pose a harm to users' safety. In order to better improve Twitter's detection of deepfakes and manipulated media, Twitter has asked users who are interested in partnering with them to work on deepfake detection solutions to fill out a form (that is due 27 November 2020).

Facebook has taken efforts towards encouraging the creation of deepfakes in order to develop state of the art deepfake detection software. Facebook was the prominent partner in hosting the Deepfake Detection Challenge (DFDC), held December 2019, to 2114 participants who generated more than 35,000 models. The top performing models with the highest detection accuracy were analyzed for similarities and differences; these findings are areas of interest in further research to improve and refine deepfake detection models . Facebook has also detailed that the platform will be taking down media generated with artificial intelligence used to alter an individual's speech. However, media that has been edited to alter the order or context of words in one's message would remain on the site but be labeled as false, since it was not generated by artificial intelligence.

Most of the academic research surrounding deepfakes focuses on the detection deepfake videos. One approach to deepfake detection is to use algorithms to recognize patterns and pick up subtle inconsistencies that arise in deepfake videos. For example, researchers have developed automatic systems that examine videos for errors such as irregular blinking patterns of lighting. This approach has been criticized because deepfake detection is characterized by a " moving goal post" where the production of deepfakes continues to change and improve as algorithms to detect deepfakes improve. In order to assess the most effective algorithms for detecting deepfakes, a coalition of leading technology companies hosted the Deepfake Detection Challenge to accelerate the technology for identifying manipulated content. The winning model of the Deepfake Detection Challenge was 65% accurate on the holdout set of 4,000 videos. A team at Massachusetts Institute of Technology published a paper in December 2021 demonstrating that ordinary humans are 69-72% accurate at identifying a random sample of 50 of these videos.

A team at the University of Buffalo published a paper in October 2020 outlining their technique of using reflections of light in the eyes of those depicted to spot deepfakes with a high rate of success, even without the use of an AI detection tool, at least for the time being.

Another team led by Wael AbdAlmageed with Visual Intelligence and Multimedia Analytics Laboratory (VIMAL) of the Information Sciences Institute at the University Of Southern California developed two generations of deepfake detectors based on convolutional neural networks. The first generation used recurrent neural networks to spot spatio-temporal inconsistencies to identify visual artifacts left by the deepfake generation process. The algorithm archived 96% accuracy on FaceForensics++; the only large-scale deepfake benchmark available at that time. The second generation used end-to-end deep networks to differentiate between artifacts and high-level semantic facial information using two-branch networks. The first branch propagates color information while the other branch suppresses facial content and amplifies low-level frequencies using Laplacian of Gaussian (LoG). Further, they included a new loss function that learns a compact representation of bona fide faces, while dispersing the representations (i.e. features) of deepfakes. VIMAL's approach showed state-of-the-art performance on FaceForensics++ and Celeb-DF benchmarks, and on March 16th, 2022 (the same day of the release), was used to identify the deepfake of Volodymyr Zelensky out-of-the-box without any retraining or knowledge of the algorithm with which the deepfake was created.

Other techniques use blockchain to verify the source of the media. Videos will have to be verified through the ledger before they are shown on social media platforms. With this technology, only videos from trusted sources would be approved, decreasing the spread of possibly harmful deepfake media.

Digitally signing of all video and imagery by cameras and video cameras, including smartphone cameras, was suggested to fight deepfakes. That allows tracing every photograph or video back to its original owner that can be used to pursue dissidents.

One easy way to uncover deepfake video calls consists in asking the caller to turn sideways.

Since 2017, Samantha Cole of Vice published a series of articles covering news surrounding deepfake pornography. On 31 January 2018, Gfycat began removing all deepfakes from its site. On Reddit, the r/deepfakes subreddit was banned on 7 February 2018, due to the policy violation of "involuntary pornography". In the same month, representatives from Twitter stated that they would suspend accounts suspected of posting non-consensual deepfake content. Chat site Discord has taken action against deepfakes in the past, and has taken a general stance against deepfakes. In September 2018, Google added "involuntary synthetic pornographic imagery" to its ban list, allowing anyone to request the block of results showing their fake nudes.[check quotation syntax ] In February 2018, Pornhub said that it would ban deepfake videos on its website because it is considered "non consensual content" which violates their terms of service. They also stated previously to Mashable that they will take down content flagged as deepfakes. Writers from Motherboard from Buzzfeed News reported that searching "deepfakes" on Pornhub still returned multiple recent deepfake videos.

Facebook has previously stated that they would not remove deepfakes from their platforms. The videos will instead be flagged as fake by third-parties and then have a lessened priority in user's feeds. This response was prompted in June 2019 after a deepfake featuring a 2016 video of Mark Zuckerberg circulated on Facebook and Instagram.

In May 2022, Google officially changed the terms of service for their Jupyter Notebook colabs, banning the use of their colab service for the purpose of creating deepfakes. This came a few days after a VICE article had been published, claiming that "most deepfakes are non-consensual porn" and that the main use of popular deepfake software DeepFaceLab (DFL), "the most important technology powering the vast majority of this generation of deepfakes" which often was used in combination with Google colabs, would be to create non-consensual pornography, by pointing to the fact that among many other well-known examples of third-party DFL implementations such as deepfakes commissioned by The Walt Disney Company, official music videos, and web series Sassy Justice by the creators of , DFL's GitHub page also links to deepfake porn website Mr. Deepfake and participants of the DFL Discord server also participate on Mr. Deepfakes.

In the United States, there have been some responses to the problems posed by deepfakes. In 2018, the Malicious Deep Fake Prohibition Act was introduced to the US Senate, and in 2019 the DEEPFAKES Accountability Act was introduced in the House of Representatives. Several states have also introduced legislation regarding deepfakes, including Virginia, Texas, California, and New York. On 3 October 2019, California governor Gavin Newsom signed into law Assembly Bills No. 602 and No. 730. Assembly Bill No. 602 provides individuals targeted by sexually explicit deepfake content made without their consent with a cause of action against the content's creator. Assembly Bill No. 730 prohibits the distribution of malicious deepfake audio or visual media targeting a candidate running for public office within 60 days of their election.

In November 2019 China announced that deepfakes and other synthetically faked footage should bear a clear notice about their fakeness starting in 2020. Failure to comply could be considered a crime the Cyberspace Administration of China stated on its website. The Chinese government seems to be reserving the right to prosecute both users and online video platforms failing to abide by the rules.

In the United Kingdom, producers of deepfake material can be prosecuted for harassment, but there are calls to make deepfake a specific crime; in the United States, where charges as varied as identity theft, cyberstalking, and revenge porn have been pursued, the notion of a more comprehensive statute has also been discussed.

In Canada, the Communications Security Establishment released a report which said that deepfakes could be used to interfere in Canadian politics, particularly to discredit politicians and influence voters. As a result, there are multiple ways for citizens in Canada to deal with deepfakes if they are targeted by them.

In 2018, the Defense Advanced Research Projects Agency (DARPA) funded a project where individuals will compete to create AI-generated videos, audio, and images as well as automated tools to detect these deepfakes. In 2019, DARPA hosted a "proposers day" for a project affiliated with the Semantic Forensics Program where researchers were driven to prevent viral spread of AI-manipulated media. DARPA and the Semantic Forensics Program were also working together to detect AI-manipulated media through efforts in training computers to utilize common sense, logical reasoning. In 2020 DARPA created a Media Forensics (MediFor) program, to detect and mitigate the increasing harm that deepfakes and AI-generated media posed, to provide information regarding how the media was created and to address and emphasize the consequential role of deepfakes and their influence upon decision making.

The 1986 Mid-December issue of magazine published the novelette "Picaper" by Jack Wodhams. Its plot revolves around digitally enhanced or digitally generated videos produced by skilled hackers serving unscrupulous lawyers and political figures.

The 1987 film The Running Man starring Arnold Schwarzenegger depicts an autocratic government using computers to digitally replace the faces of actors with those of wanted fugitives to make it appear the fugitives had been neutralized.

In the 1992 techno-thriller A Philosophical Investigation by Philip Kerr, "Wittgenstein", the main character and a serial killer, makes use of both a software similar to deepfake and a virtual reality suit for having sex with an avatar of Isadora "Jake" Jakowicz, the female police lieutenant assigned to catch him.

The 1993 film Rising Sun starring Sean Connery and Wesley Snipes depicts another character, Jingo Asakuma, who reveals that a computer disc has digitally altered personal identities to implicate a competitor.

Deepfake technology is part of the plot of the 2019 BBC One drama The Capture. The series follows British ex-soldier Shaun Emery, who is accused of assaulting and abducting his barrister. Expertly doctored CCTV footage is used to set him up and mislead the police investigating him.

Al Davis vs. the NFL: The narrative structure of this 2021 documentary, part of ESPN's documentary series, uses deepfake versions of the film's two central characters, both deceased-Al Davis, who owned the Las Vegas Raiders during the team's tenure in Oakland and Los Angeles, and Pete Rozelle, the NFL commissioner who frequently clashed with Davis.

Deepfake technology is featured in "Impawster Syndrome", the 57th episode of the Canadian police series , first broadcast on 6 January 2022, in which a member of the St. John's police team is investigated on suspicion of robbery and assault due to doctored CCTV footage using his likeness.

Using deepfake technology in his music video for his 2022 single, "The Heart Part 5", musician Kendrick Lamar transformed into figures resembling Nipsey Hussle, O.J. Simpson, and Kanye West, among others. The deepfake technology in the video was created by Deep Voodoo, a studio led by Trey Parker and Matt Stone, who created .

Aloe Blacc honored his long-time collaborator Avicii four years after his death by performing their song "Wake Me Up" in English, Spanish, and Mandarin, using deepfake technologies.

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Deepfakes (a portmanteau of " deep learning" and "fake") are synthetic media in which a person in an existing image or video is replaced with someone else's likeness. While the act of creating fake content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content that can more easily deceive.