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how dangerous are deepfakes

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How Dangerous are Deepfakes? | Explained

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Very realistic Tom Cruise Deepfake | AI Tom Cruise

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You Won’t Believe What Obama Says In This Video! 😉

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It’s Getting Harder to Spot a Deep Fake Video

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fake news

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As we're well aware, establishing what is real and what is fake online has become increasingly difficult over the last few years.

As social media has grown, so too has the ability to deliberately spread false information to a potential global audience.

As Winston Churchill is falsely (and ironically) attributed to saying, "A lie can travel halfway around the world before the truth has put its boots on."

Indeed philosophers will remind us that often there is no objective truth, but merely different interpretations of events.

Epistemological definitions aside, it looks like the pinnacle of 'fake news' is hopefully behind us.

That is to say, the articles that contain not an ounce of truth and which are designed to manipulate the network effect of social media and psychology of social sharing are on the wane.

Facebook, Twitter et al are all aware of the ramifications of fake news and how these news sites played - and continue to play - a prominent role in the amplification of it.

The introduction of new features by Facebook to stop fake news from spreading on its platform seem to be working according to a recent independent report [pdf] by Stanford University.

The report also says Twitter's attempts at combatting fake news have not.

Nevertheless, it seems that 'The Great Fake News Era' of 2015-18 is coming to a slow and drawn-out end as people become savvier on what they share and as the social media giants continue to bow to pressure from governments around the world.

Deepfake technology: Fake news 2.0

In the era of manipulated images and, declining-but-still-very-present, fake news, there is a new kind of technology on the horizon. One that can be potentially more problematic than the other two combined.

Deepfake is a technology based on machine learning which is used to produce or alter video to make it look like something happened that did not.

The term deepfake is an amalgamation of both 'deep learning' and 'fake' and is a manipulation method that's been used in the studios of Hollywood for years.

Perhaps one of the earliest uses (and hence the leading image in this article) was in the film Forrest Gump when the hero of the story met John F. Kennedy and alerted him to the fact that he had to "go pee."

Superman, Star Wars and many other films since have used this technology to enhance the cinematic experience.

Like most technology, the barriers to entry are coming down as consumer technology becomes more powerful and begins integrating with machine learning algorithms.

Video is usually more difficult to manipulate than photographs because it's moving imagery so, unlike a photograph, there a multiple frames to edit.

Of course, video has never been manipulation-proof completely. Anyone who knows how to use editing software can chop a clip together to make it seem someone said something they didn't. This still goes on online regularly.

But to actually put words in the mouth of another person who is face-forward on camera was exclusively reserved for the CGI studios of Hollywood. Not anymore.

Deepfakes explained in a nutshell

Remember a couple of years ago when everyone got infatuated with Snapchat filters, especially the dog filter that gave you loppy ears, a snout and particularly long tongue?

This is a form of deepfake technology and they come in two varieties:

Superimposing someone's face onto a body so it looks like they did something they never did

Taking a speech and manipulating it onto an unsuspecting person's face so it looks like they said it

Deepfake technology has become fully automated. To make a deepfake video all that's required are a selection of the images of the person's face being replaced and the person's face who is being superimposed.

This has lowered the barriers to anyone using this technology and assuming the person being deepfaked is famous a quick image search online provide all the headshots needed.

Deepfake technology is being pioneered by the porn industry

Much like online and streaming video was pioneered by the porn industry early on, it's probably no surprise that deepfake tech is being used in the porn industry.

Superimposing a celebrity's face onto another person's body is a common use case. This is an infringement on someone's identity and is, of course, a type of fake news story in of itself.

A number of porn sites claim to have banned deepfake videos uploaded to their sites. As too have Twitter and Reddit. The latter of which had two popular subreddits: r/Deepfakes and r/Celebfakes which have both been removed by the company.

Deepfakes are arguably the biggest technological danger when it comes to politics and diplomacy

Given how easy it is to create a deepfake video, it's likely they will be used with negative political intent. This could be one of the biggest technological danger we face in politics.

Politicians are incredibly easy subjects for deepfakes because they are often standing face-forward on camera.

Deepfakes could be used to create fake sex scandals for political figures, business leaders, celebrities and well-known people in the public eye.

Or, in fact, they can be used as an excuse when someone gets caught doing what they shouldn't be.

"It was a set up to smear me using deepfake video!" they might say.

Here's an example of a deepfake video created by BuzzFeed using actor and comedian, Jordan Peele voicing over Barack Obama.

"Photoshop for audio"

You might say while Peele is a good impersonator of Obama, anyone who has heard the ex-president speak over the last ten years will know that it's not his voice.

While most level-headed people could differentiate between an impersonator's voice and Obama's, a new technology that complements deepfake video and is perhaps equally as dangerous was announced a couple of years ago.

Like the Obama example, most deepfakes are created using an impersonation which means you need someone like Jordan Peele onhand to provide the necessary skill.

This could change however since the Adobe announcement of 'Voco', its new software program which has been dubbed "Photoshop for audio" because of its ability to replicate someone's voice pitch-perfect.

Using a learning algorithm to analyse speech patterns, all Voco requires is 40 minutes of someone speaking to be able to recreate it.

Once that data is collected a user can simply type the words they want and match it to a deepfake video.

Below is the Adobe Voco announcement including Obama impersonator Jordan Peele once again.

Spotting deepfake videos

Adobe Voco is still in prototype phase and when it comes to deepfake video there are often tell-tale glitches you can see, particularly when a subject moves their head or tilts it to an angle.

For now, and as far as we know, someone can likely identify a deepfake video when shown to them. The issue we face moving forward is the software is getting better and making it more difficult to tell fact from fiction.

Technology in all its forms improves over time and it's no different here. Soon it will be very difficult to tell a fake video from a real one which will create all kinds of problems especially when it comes to discovering that all-important question: what is real?

In the UK use of deepfakes for so-called revenge porn could be banned by the British government but trying to ban something that spreads on the internet is often counterintuitive.

Many people, rich, famous or otherwise, have discovered the power of the Streisand Effect when trying to prevent something from spreading online.

The future of deepfakes

Some have speculated that deepfake video could create more upheaval than the fake news sites of 2015-18.

They have the potential to cause chaos among country states and governments as well as business leaders, celebrities and everyday people like us.

Once the tech is so good you can't tell the difference between a deepfake video and a real one they can be weaponised to destroy reputations and create friction among communities large and small.

Thankfully people are becoming more aware of how fake information spreads online. For the more rational thinkers, there's a greater scepticism when it comes to believing what they read and see in social media and elsewhere.

A recent report found that young people are better at spotting fake news online than the older generation.

This is the generation that will likely have to deal with deciphering a deepfake video from a real one and it looks like they're ready for it.

Let's hope we all are.

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Deepfakes are the evolution of fake news and are equally as dangerous

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As we're well aware, establishing what is real and what is fake online has become increasingly difficult over the last few years. As social media has grown, so too has the ability to deliberately spread false information to a potential global audience.

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audio scams

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The evolution of modern technology has brought many innovations, and one in particular is shaking up media landscape: deepfakes. Deepfakes are videos, images or audio recordings that have been manipulated by AI technology. In a deepfake, an individual can be presented as saying or doing something that didn't actually happen.

Deepfake content is highly convincing, and the ongoing development of deepfake tech has made it more difficult to discern between real and fake content. While deepfake tech is still relatively new, we continue to see its role in emerging fraud and cybercrime trends. This has become a growing concern among consumers and organizations, as deepfakes are exploited by criminals to carry out social engineering attacks, the spread of misinformation and fraud scams.

The cost of a deepfake scam was estimated to exceed $250 million in 2020, and this form of technology is still in its early stages. There's no doubt that as deepfake technology evolves, so will the sophistication of how criminals exploit this tech to attack businesses and consumers alike. Read on to learn more or jump to the infographic below.

What is a Deepfake?

A deepfake is a form of media that overlays an existing image or video with AI-generated content that resembles someone's voice or appearance. Commonly referred to as a form of "synthetic media," deepfakes mimic people's faces, movements and voices with such accuracy that they're often impossible to tell apart from the real thing.

Thanks to highly sophisticated machine learning algorithms, biometrics like facial expressions and the pitch of the human voice can be manipulated to create realistic depictions of events that never took place. While not all deepfakes are used with malicious intent, this form of digital impersonation has often been used to create fake videos and convincingly real audio recordings of people doing or saying malicious things, prompting privacy concerns and fears of deception.

How Does Deepfake Technology Work?

The key component to creating a deepfake is machine learning. Deepfakes rely on AI computer systems called artificial neural networks, which are based loosely on the human brain and designed to recognize patterns in data. This is where the creation of a deepfake begins.

To create a deepfake video, the creator starts by feeding hundreds of hours of real video footage to the artificial neural network in order to "train" the computer to identify detailed patterns and characteristics of a person. This is done to give the algorithm a realistic understanding of what that person looks like from different angles.

The next step involves combining the trained neural network with computer-graphics techniques to overlay the real footage of a person with the AI-synthesized facial and speech patterns derived from the neural network data.

While many believe that creating a deepfake requires sophisticated tools and expert skills, this isn't the case-they can be made even with basic computer-graphics skills. More convincing deepfakes do require more advanced techniques, but all you really need is access to video or audio footage of someone. This is especially accessible given the staggering amount of media available today, leading to an ample amount of source material to feed the algorithm and generate a realistic deepfake.

What's the Risk? 3 Types of Deepfake Fraud

The rapid advancement of deepfake technology has created an opportunity for tech-savvy criminals to enable serious financial harm. From identity theft and the spread of public misinformation to corporate extortion, fraud and automated cyberattacks, deepfake technology has introduced a new breed of media that bad actors are using to their advantage. Below are a few of the ways criminals commit deepfake fraud.

Ghost Fraud

Ghost fraud occurs when a criminal steals the data of a deceased person and in order to impersonate them for financial gain. The stolen identity might be used to gain access to online services and accounts or to apply for things like credit cards and loans.

New Account Fraud

Also referred to as application fraud, new account fraud involves using stolen or fake identities for the purpose of opening new bank accounts. Once a criminal has opened an account, they can wreak serious financial damage by maxing out credit cards or taking out loans they have no intention of paying back.

Synthetic Identity Fraud

Synthetic identity fraud is a more complex method of fraud that's typically more difficult to spot. Rather than exploiting the stolen identity of a single person, criminals mine for information and identities of multiple people to create a "person" who doesn't actually exist. This manufactured identity is then used for large transactions or new credit applications.

Examples of Deepfake Attacks

Recent innovations have substantially reduced the amount of time and data required to create highly realistic deepfakes. As deepfakes become more accessible, the number of known attacks will likely continue to rise. The examples below offer a look into what deepfake technology is capable of.

Energy Firm CEO Attack

This deepfake scam from 2019 is the first known deepfake attack to occur, and it illustrates the unfortunate dark side of deepfake technology's capabilities. In a classic case of corporate extortion, the CEO of an energy firm took a phone call from who he thought was his boss and chief executive of the firm. In reality, the voice was actually an AI-generated deepfake voice-which is why he promptly cooperated with the urgent request to transfer $243,000 within the hour.

Tech Firm Attack

In this unsuccessful audio deepfake attempt from 2020, a tech firm employee received a strange voicemail from someone who sounded like the firm's CEO. The message was a request for "immediate assistance to finalize an urgent business deal," and the employee followed his suspicions and flagged it to the firm's legal department.

While this deepfake attack was ultimately unsuccessful, it's an important window into the types of attacks we can expect to see more of as technology advances and tools become more widely available.

How to Detect a Deepfake Scam

While the scammer in the tech firm's deepfake attack sounded similar to the CEO he was impersonating, the slight robotic tone of the deepfake voice is what ultimately triggered the employee's suspicions. Several similar telltale signs can tip you off to a potential audio or video deepfake:

Unnatural speech cadence

Robotic tone

Unnatural blinking or movement

Lip movements are out of sync with speech

Poor audio or video quality

Organizations can combat the threat of a deepfake attack mainly by educating their workforce on how to recognize potential audio deepfakes. Most often, educating employees on cybersecurity measures is an organization's first line of defense against cyberattacks. Providing guidance on company protocol and having a system in place for internally verifying suspicious communications is an important place to start when it comes to organizational security.

The Future of Deepfakes

Deepfake technology has seen an incredible rise in just a few short years. While deepfake detection tools are improving, so are the capabilities of deepfakes themselves. As a result, federal legislative efforts have recently focused on further research into understanding deepfake technologies, and more state-level laws are emerging to provide support for victims.

The nation's first deferral law pertaining to deepfakes was passed in 2020. It criminalized the creation and distribution of deepfake videos that aren't properly labeled, and established a plan for further research and development of deepfake detection tools.

While deepfakes certainly aren't the first threat to cybersecurity, they represent a growing challenge that will require ongoing research to prevent criminals from exploiting it. Organizations and individuals alike will need to seek out new ways to properly secure their data and defend against increasingly sophisticated cyberattacks.

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Deepfake Fraud: Security Threats Behind Artificial Faces - Panda Security Mediacenter

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The evolution of modern technology has brought many innovations, and one in particular is shaking up media landscape: deepfakes. Deepfakes are videos, images or audio recordings that have been manipulated by AI technology. In a deepfake, an individual can be presented as saying or doing something that didn't actually happen.

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deepfake scan pics

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Deepfake porn and blackmail - the new scourge of the internet?

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Every now and again something arises from the shady regions of the internet that makes us all sit up and take notice. When the phenomenon of the deepfake first arose in 2017, it was an immediate sensation.

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deepfake pornography

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david beckham advert

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Pros and Cons: Deepfake technology and AI avatars - Springwise

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While the negative effects of deepfakes are easy and frightening to imagine, there are also some advantages to using the technology for productive purposes.

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rights to persona

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A big issue is compensation for the person what created, if you like, the “brand” that made you care in the first place.

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nick fury de age

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Samuel L. Jackson is de-aged by about 25 years in "Captain Marvel."

Around 500 shots of Jackson needed to be de-aged for the movie, the most de-aging that has been done in a Marvel project.

The VFX team referenced Jackson's '90s movies, including "The Negotiator" and "Star Wars: The Phantom Menace" to make sure he looked just right.

Visit Insider's homepage for more stories.

Clark Gregg and Samuel L. Jackson were both de-aged in " Captain Marvel" to look years younger for the film that takes place in 1995.

Jackson's look was one of the toughest to nail because he appears in so much of the superhero movie.

"It was actually a big challenge to get him 25 years younger and look the way that he did," Marvel visual effects supervisor Janelle Croshaw Ralla told Insider during a press day at the Walt Disney Studios lot in Burbank, California. "Each scene had its own methodology."

The visual effects team referenced a number of Jackson's older movies, including the "Star Wars" prequels, to bring specific moments to life.

"We referenced 'Diehard with a Vengeance,' 'The Negotiator,' 'Sphere' a little bit, [and] even 'The Phantom Menace' a little bit for a couple of shots," said Croshaw Ralla of the '90s films they went through.

"It was pretty amazing," Croshaw Ralla added. "You could go back to those movies and find exact frames where he looked exactly the same, like the exact same expression. He was just aged."

Late-night host Jimmy Kimmel actually put images of Jackson from 1998's "The Negotiator" and "Captain Marvel" side by side for viewers to see the resemblance.

The de-aging of Samuel L. Jackson's Nick Fury was more complex than what we've seen in any other Marvel movie or show

We've seen de-aging in Marvel movies before, but not like this.

Robert Downey Jr.'s Tony Stark was de-aged in a small moment in "Captain America: Civil War" and Michael Douglas was de-aged in both "Ant-Man and the Wasp" and "Avengers: Endgame" briefly.

Gregg's Agent Coulson was de-aged in about 50 shots of "Captain Marvel."

There are 10 times as many shots of Jackson that were de-aged for the Marvel film that required the work of three VFX houses: Rising Sun Pictures, Lola, and Screen Scene Visual Effects (SSVFX).

"There were over 500 shots. So he was by far our main focus," Croshaw Ralla said of one of the biggest challenges of bringing "Captain Marvel" to screen.

"This actually was handled very differently from other Marvel shows because other Marvel shows have had a lot less de-aging. It wasn't throughout the whole film," she added.

Croshaw Ralla said in other cases they simply use a body double to capture skin texture and to help with the de-aging process on-screen. That wasn't what was done with Jackson in "Captain Marvel."

"In this one, we just couldn't afford to do that," said Croshaw Ralla. "By cutting out the body double it also cut down the production time on set by half. And so we did a test early on with Lola where they did a beautiful test with Sam Jackson."

The VFX team convinced the filmmakers to go this route for a more noninvasive approach to Jackson's de-aging on set.

"Sam could just act the way he wanted to act and we basically fixed it in post [production]," said Croshaw Ralla.

Making sure they're not taking away from Jackson's performance

There are so many tiny subtle things that go into the de-aging process that audiences may not even realize, which include changing up hands and posture and thinning people. Those are the details that made it one of the most challenging aspects of the movie other than getting Goose the Cat just right.

"It's easy to go into the uncanny valley when you're taking 25 years off of somebody," said Croshaw Ralla. "You're literally modifying so much from the whole body to the face that it's easy to all of a sudden take the viewer out of the scene and have it not match from scene to scene or go too far and it doesn't look like him anymore."

"The most important thing is that you're not taking away from his performance," added The Third Floor visual effects supervisor Shannon Justison. "You want Sam Jackson's performance to come through at all times. That's our number one concern."

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The 'Captain Marvel' visual effects team referenced movies including 'Star Wars: The Phantom Menace' to de-age Samuel L. Jackson in the film

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Samuel L. Jackson is de-aged by about 25 years in "Captain Marvel." Around 500 shots of Jackson needed to be de-aged for the movie, the most de-aging that has been done in a Marvel project. The VFX team referenced Jackson's '90s movies, including "The Negotiator" and "Star Wars: The Phantom Menace" to make sure he looked just right.

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dialogue

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deepfake coding

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Have you heard of deepfake videos? They've gained a lot of attention lately for their ability to mimic real people and make them say or do things they've never done. Experts say that this software can be used to undermine democracy, spread false information, and even completely change our perception of what is true or not - but some of us just want to make memes.

This article will give you an overview of the different ways you can make a deepfakes using various deepfake software available today.

But most importantly, before we begin, don't use Deepfake technology unethically. After trying it out ourselves, we still don't think the tech is quite 100% believable yet, BUT it still can land you in a lot of trouble and be harmful to the person you are mimicking. As long as you keep it lighthearted and avoid misusing the software, deepfakes can be a great way to get a laugh or parody a celebrity. Here is the deepfake music video our team made of Elon Musk:

Exploring Deepfake Software & Apps

We started our deepfake journey because we wanted to incorporate famous people into a New Mark music video. It seemed like a fun experiment and a great way to make an engaging music video. The intention, of course, was not to actually convince people these celebrities had anything to do with the video, but rather to make a fun (and positively skewed) story of someone.

With that goal in mind, we started down the path of testing out various deepfake softwares and apps - trying to find the best balance between realism and ease of creation.

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We saw a lot of different software offering deepfake capabilities, some with varying difficulties, and others no harder to use than a regular old Snapchat filter. This will be an overview of what we found, how it's used, and how you can create one too.

Are deepfakes legal?

Deepfakes are perfectly legal if you don't abuse them. Even though this is a relatively new technology, the laws around using someone's likeness, slander, and misrepresentation have been around for a long time.

You can't use deepfakes to sell a product. Can you imagine if instead of some cereal commercial paying a celebrity to promote their product, the company just used a deepfake? There would be a lot of legal backlash. The same goes for you and me, don't use deepfakes to sell products.

Don't use deepfakes to slander. Using deepfakes to slander is not only illegal, but it's also highly unethical. You shouldn't use deepfakes to try and ruin someone's reputation or make them say something they wouldn't say under the pretense that it's real.

Parodies are ok. The good news for meme-loving people? Parodies are an acceptable use of deepfakes and can make for some excellent content. The key here is to make sure your video is obviously a parody and can't be misunderstood as a real video. Use a lot of caution when parodying someone you know or a famous celebrity.

Deepfake Apps vs. Deepfake Softwares

Deepfake apps are incredibly easy to use but won't give you the most realistic results. Almost anyone with a smartphone can use them and they don't require you to have any background knowledge in coding.

Deepfake software, on the other hand, is harder to use. But, you get a lot more flexibility in what you can do with your videos than the limited capabilities of simplified apps. If you overcome the technical hurdles of using deepfake software, you'll get better results and greater creative freedom.

DeepFake Softwares

We'll cover the leading deepfake software in this article: DeepFace Labs, Faceswap, First Order Model, and Wav2Lip. We have personal experience using all of them, except for Faceswap.

To use deepfake software, you should come into ready to learn a little python coding language. You will be working with AI as your videos self-learn to become smooth and realistic. In other words, your computer will need to train itself.

You also need a dedicated graphics card or a virtual GPU (Google Cloud is a popular service). Deepfake software is demanding for any computer and requires you to use a sophisticated system to keep it running smoothly and without any errors.

First-Order-Model

The first-order-model is a GitHub library and you can run it through a Google Collab doc. This was our first stumble into deepfakes and machine learning, and it provided some great results.

You begin by using a source image and a driving video. You extract motions from the driving video and use them to generate into the source image. This relies on anchor points to work, meaning you need to use sources with movement inputs to come up with a good result. You can use it for stickers, human motion, and robotic movements.

Starting from square 0, like literally, having to google "What are deepfakes?" and then "What is google collab?" - we were able to create something passable in about 4 hours.

The process for First-order-model involves filming a video of yourself or someone you know, and then the eye, lip, and head movements can be transferred onto a still image. It's great because rather than creating a "fake" image, you're really just manipulating and distorting an actual image. This makes the results look pretty realistic but also the process limits you to one static scene and background.

Once you have it down, you can create them in about 10 minutes.

Here is an overview of the software from the creator:

Here is an excellent tutorial to follow along with:

After spending an afternoon creating the first result and pushing past a few mess-ups, we can now create a basic deepfake using the First Order Model in no time.

The problem with this method, is that while it's realistic and relatively easy, you are limited to manipulating a static image of the person. It's not super flexible - they must remain in the scene and relative position that they are in within the photo.

Wav2Lip Model

This is also a GitHub library and is used to manipulate images instead of recreating a deepfake.

Wave2Lip is different because it is a model that has already been trained with specific lip-syncing data. All you have to do is match a .wav file with an image and that image will then lipsync to the wav file. Hence, the name wav2lip.

We tried using Wav2Lip on one of our videos with a lot of fast words, and the results were terrible. It did work pretty well when the words in the audio file were slow and deliberate. So if that's all you need, Wav2Lip might be the way to go. The music backing track (like drums and stuff) also likely confused the computer, making it try to lip sync the mouth with the beat and the words.

Here's an excellent tutorial to learn more:

DeepFaceLab

This is the high-end "you're making a real DeepFake" software. Installing the software and the correct dependencies is difficult if you don't have programming experience. You can learn most of what you need to know from the Github page.

You can also use this DeepFaceLab tutorial to help walk you through extraction, filtering out unusable pictures or videos, and the commands you need to train and run the software.

We spent an afternoon trying to figure this out and got pretty far. Googling led us to a helpful community of machine learning experts. Strapped for time and wanting the best results for the New Mark video, we eventually enlisted a collaborator from that community to help with the full head swap.

Deepface Lab creates incredible results and is learnable by a novice, but it takes time, and expertise (that you can learn). Even if you have programing knowledge the labeling of images and training of models can take a lot of your time and a lot of GPU time to simply run and teach the algorithm how and what to modify.

In short, Deepfacelab is difficult to use but will get you the most professional results available today.

Faceswap

Faceswap is another high-quality software for creating realistic deepfakes. It can run with Tensorflow, Keras, and Python. It's also supported on Windows, macOS, and Linux. So whatever operating system or coding background you come from, this can be a great option.

You can learn most of what you need to know from their GitHub page and follow along with any of the online video and blog tutorials to get started. Faceswap, like most deepfake software for your computer, requires a bit of a learning curve.

There is no shortage of hilarious examples that use Faceswap to develop realistic deepfakes that incorporate movements and source images well. If you have a background knowledge in coding, either DeepFaceLab or Faceswap would make a great choice for making the most realistic deepfakes.

Deepfake Apps

Deepfake apps are something you can download directly to your phone and create a "Deepfake" with in just a few clicks of a button.

The plus side is that basically anyone can use them. It usually isn't more complicated than applying a Snapchat filter. The downside is that the output is generally limited or quite obviously fake and made for simple gags.

In a few years, deepfake apps will continue to get better and the results could be a lot more realistic. Eventually, this will remove the need to use Deepfake software that requires users to come in with a background knowledge of coding. Whether that's a good thing or not, ehh... not sure.

Reface DeepFake

The Reface app is easy to use and great for making memes. All you need to do is take a selfy or use a picture from your photo library, and then you can select a video to swap faces with. You can use this app for free as long as you're ok with sitting through some ads.

Reface comes with preloaded videos of celebrities, movie scenes, and memes for you to swap faces with. Some videos let you change faces with more than one person, letting you create a hilarious scene.

Reflect DeepFake

Reflect is another simplified version of deepfake software that lets us swap faces with any pictures and automatically adjusts smoothness, color, and texture to create a more realistic effect. All you need is two pictures.

You don't even have to use pictures of real people. You can use statues, paintings, memes, cartoon characters, or anything else with a face. You can use the app for free, but paying will remove watermarks and ads.

Zao DeepFake

If you're looking for something a little more involved that gives you options to edit videos by changing backgrounds and even removing entire elements, then Zao would make a great option. The Zao deepfake app was released in China and is owned by the same company that runs the "Chinese Tinder" Momo.

There is one downside though. If you're concerned about your privacy or how this company will use your face and any user-generated content, you should probably stay away from this one. In their user agreement, the company gets to use your face for whatever they want, however they want.

Jiggy DeepFake

Do you want to make anyone dance? That's what the Jiggy app does, combining deepfake software with footage of people dancing or making hilarious moves. All you need to get started is a picture of any face you want to swap into your video.

You can change dance moves, the body you swap with, and even the background of your video. With all the options to customize, you can create a unique-looking video, unlike what you can do with many other apps' limited features.

Morphin DeepFake

The Morphin deepfake app focuses on swapping faces with gifs and memes. You start by scanning your face when you first use the app, making sure to keep a neutral expression. By keeping your face scan without emotions or gestures, the app will be able to morph your face into any emotion and even make it look like your speaking.

This is another incredibly easy app to use and you'll be able to create memes without any knowledge going into it. The app will guide you through the process of getting a good face scan, and let you swap faces with any of its preloaded memes and gifs.

Ready to make your own deepfakes?

Now that you know what you can use to make a deepfake and how you can make one, you're ready to make some meme gold. If you have some background in coding, we recommend going with true deepfake software for better and more realistic results. If you don't have coding experience, you can still use deepfake apps or learn!

Thanks for reading!

Mark loves making videos, thinking, tinkering with futuristic tech, running experiments and music. Peer Through Media allows him to do all of those things.

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The Best DeepFake Software - How To Make A Deepfake

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Have you heard of deepfake videos? They've gained a lot of attention lately for their ability to mimic real people and make them say or do things they've never done. Experts say that this software can be used to undermine democracy, spread false information, and even completely change our perception of what is true or not - but some of us just want to make memes.

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how deepfakes are made

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You can read all the posts in this series here:

Photoshop and After Effects are used every day by professionals, but that does not mean that just installing either of them is all it takes to create photorealistic images and videos. Likewise, creating realistic face-swapped videos is hard. Like any artistic endeavour, the final result is a mixture of talent, commitment and right tools.

There are two critical processes involved in the making of any face-swap video: the training and the creation. Training is the process which (literally!) trains a neural network to deconstruct and reconstruct faces. Since the release of face-swapping technology, there have been countless discussions online on how to train a network to achieve photorealistic results. Let's start by analysing the problem from a Machine Learning point of view.

The task of the neural network is to encode an image from person A, and reconstruct one with similar features to resemble another person B. Imagine you had a picture of a person A, and had to manually edit it to make it look like person B. The complexity of such a task largely depends on how different the two faces are. This is easy to understand, as more "changes" are necessary to convert A into B. The neural network works in a similar fashion, as a more complex mapping has to be learned.

What is the simplest task that a face-swapping neural network can be asked to perform? Because it is built on autoencoders, is to reconstruct face A from face A. This might seem trivial at first, but keep in mind that even morphing between images from the same person is far from being trivial. What makes the task easier, however, is that the autoencoders can focus on the facial expressions, rather than having to re-adapt to an entirely different bone structure, skin colour, and so on. This explains why some of the most well-crafted deepfakes feature people with similar faces.

The problem, however, goes beyond just faces. The neural network is not specifically designed to recognise them. Different lighting conditions and angles raise the complexity of the problem, as more steps are required not just to adjust the faces, but also to identify the relevant features. If the frames used for the training process exhibit high variability in the lighting conditions, then the encoder will be forced to include information about them in the latent representation of the face. This takes up space that could be used to better represent facial expressions, hence reducing the overall quality. This is called underfitting, and it indicates that the problem is too complex to be fully captured with the model chosen.

While that is certainly true, we also have to battle the opposite problem. If all the images provided look the same, the training process might fail to capture their true variability. The result is that the neural network will learn to reproduce the same image, rather than the same face. This is a problem called overfitting.

Training for Low-End GPUs (2GB)

So what is better? Including images that are very similar to simplify the problem, or adding shots taken in different lighting conditions to better generalise how the face should look like? The answer is that it depends. It is impossible to know how the neural network will respond, just by saying that the pictures are taken under the same light. Generally speaking, however, the more complex a problem is, the harder it is to solve it. Unless you are an expert, you should strive to help the neural network as much as possible.

If your aim is to perform face-swap on a single clip, you do not need a neural network that is able to perform face-swap in any condition. You only need one that works just once. The easiest way to achieve that, is by reducing the complexity of the problem. You can start by selecting the right videos:

Use videos shot in similar lighting conditions. Light does not only affect the colour of a scene. It also casts shadows on faces which are hard to match. This is because reproducing realistic shadows requires a three-dimensional understanding of the face geometry, which needs to be learned from two-dimensional images.

Choose people with similar faces. This goes beyond the simple idea concept of "face". Makeup, glasses and beards make the problem much more challenging. It is particularly difficult to use faces with features that go outside the face itself (such as long beards or occluding hair), as they will be cut out.

Choose people with similar skin tones. One of the biggest downsides of face-swap is how faces, once converted by the neural network, are blended back into the video. The way it works simply smooths the edges of the image in a rather primitive way. If person A and B have very different skin tones, it will be difficult to merge them seamlessly. Keep in mind that certain implementations of face-swap adjust the colours of the reconstructed face to match the original skin tone. Such an operation, unfortunately, is performed on the entire image and will affect even areas that do not need to be corrected (such as the eyes and facial hair).

You can get good results very quickly if you use only two video clips; one for person A and one for person B, possibly shot under similar lighting conditions. After that, you can start pre-processing them:

Split frames based on face orientation. Instead of training a single network and use it on both clips, you should repeat the training process multiple times. If you are using FakeApp, the easiest way is to extract all frames from video A and B, and arrange them in three folders for frontal, three-quarter and side views.

Train different models. You can train three different, one for each face orientation, so that each neural network only has to focus on detecting and converting facial expressions, and head movements. Rotating faces is also very challenging due to occluded features which need to be "guessed".

What is left now is to split the target video and convert each segment with the model that it is more appropriate. Since the mapping to reconstruct faces should be much simpler, you can keep the settings low. This solution is perfect for people who do not have a powerful GPU and can only train neural networks with 3 layers or less.

Training for High-End GPUs (8GB)

If you have at your disposal a high-end GPU (such as an NVIDIA GTX 1080) you can push the training settings much further. This does not necessarily mean you will obtain better results, but that you can be confident the neural network will learn more complex mapping on its own.

Of the most complex feature to learn is, usually, shadows. This is because videos are taken in settings with different lighting conditions, and reproducing realistic shadows requires a three-dimensional understanding of the face structure. To learn this, your best bet is to provide enough pictures in different lighting conditions and angles. Make sure, however, that both datasets have similar angles

Different Resolutions

Most deepfakes look bad because the faces have a different resolution from the rest of the video. This creates artefacts that dramatically reduce the realism of the image.

This is caused, most of the time, by the way tools such as FakeApp works. Neural networks can only work on images that are of the same size. Once a face is detected, it is cropped and reshaped into a 256×256 image. Only the central 160×160 region is used for the training process, which is further downscaled to 64×64 pixels. Consequently, the reconstructed faces are also 64×64 pixels. When merged into a video, these newly created images are morphed back to match their original size. If you are using an HD video, it is likely the original face was larger than that, resulting in a blurred image.

The transformation causes the new face to look somehow blurred, something which is exasperated by compression artefacts.

There are two ways to resolve this issue. One is to train a neural network so that it can work with larger images. The problem, however, is not fully solved because if trained with low-resolution videos, it will be essentially fed with blurred images.

Another solution is to reduce the resolution of the video you want to face-swap, so that the target face is close to 256 pixels tall. If you up-scale the video once is converted, you will have a uniform resolution. The downside is that you are also sacrificing its original quality.

When To Stop the Training

The longer you train a model, the better it becomes. However, the better it is, the longer it takes to make a small improvement. This is problematic, as it is not trivial to decide when to stop the training process.

We can shine some light on this mystery by plotting the evolution of the score over time. In the vast majority of cases, it loosely follows an exponential decay. The chart below shows the score collected from the first 100 iterations training a neural network to convert Anisa Sanusi into Henry Hoffman.

Misleading Scores

Your objective is to create realistic deepfakes, not to reduce the score as much as possible. While it is true that lower scores usually produce better videos, this is not always the case. There are many operations you can do during the training process which will lower the score, although without improving the overall quality.

The score is calculated based on how well the neural network is able to reconstruct the face. And this check is done against a number of images taken randomly from the extracted faces. Their number depends on the batch size. A higher value means that the neural network has to match against more images. This slows down the training process, but typically results in more realistic images.

It is possible to change the batch size during training. As you can imagine, this might cause the score to fall or to rise sharply. The model (and its quality) has remained unchanged, but what has changed is how strictly we evaluate its progress.

That being said, changing the batch size could be a valuable tool. If done properly, you can slowly raise it so that the neural network can start from an easier task. Whether this will work on not for you is impossible to tell, as it really depends on each dataset.

Conclusion

You can read all the posts in this series here:

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How To Create The Perfect DeepFakes - Alan Zucconi

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This tutorial is for all the users who have attempted to create their own deepfakes videos, and failed. If you want to know how to make the most out of face-swap technology using FakeApp and FaceSwap, this is the tutorial for you.

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What is deepfake