Deepfakes (a portmanteau of "deep learning" and "fake") are media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks. They often combine and superimpose existing media onto source media using machine learning techniques known as autoencoders and generative adversarial networks (GANs). Deepfakes have garnered widespread attention for their uses in celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud. This has elicited responses from both industry and government to detect and limit their use.
- 1 History
- 2 Techniques
- 3 Applications
- 4 Concerns
- 5 Responses
- 6 In popular culture
- 7 References
- 8 External links
The development of deepfakes has taken place to a large extent in two settings: research at academic institutions and development by amateurs in online communities. More recently it has also been adopted by industry.
Academic research related to deepfakes lies predominantly within the field of computer vision, a subfield of computer science. 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.
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. In December 2017, Samantha Cole published an article about r/deepfakes in Vice that drew the first mainstream attention to deepfakes being shared in online communities. Six weeks later, Cole wrote in a follow-up article about the large increase in AI-assisted fake pornography. In February 2018, r/deepfakes was banned by Reddit for sharing involuntary pornography. Other websites have also banned the use of deepfakes for involuntary pornography, including the social media platform Twitter and the pornography site Pornhub. However, some websites have not yet banned Deepfake content, including 4chan and 8chan.  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 and the command line-based DeepFaceLab.
Larger companies are also starting to use deepfakes. The mobile app giant Momo created the application Zao which allows users to superimpose their face on TV and movie clips with a single picture. The Japanese AI company DataGrid made a full body deepfake that can create a person from scratch. They intend to use these for fashion and apparel.
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 the 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, anytime a defect is determined it can be corrected.
Around 96% of deepfakes on the internet feature pornography that was created without the consent of the actors or the people, often female celebrities, whose likeness was used. Deepfake pornography prominently surfaced on the Internet in 2017, particularly on Reddit. The first one that captured attention was the Daisy Ridley deepfake, which was featured in several articles. Other prominent pornographic deepfakes targeted celebrities such as Gal Gadot, Emma Watson, Masie Williams, Taylor Swift and Scarlett Johansson. As of October 2019, most of the targeted 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 which 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 June 27 the creators removed the application and refunded consumers.
Deepfakes have been used to misrepresent well-known politicians in videos. In separate videos, the face of the Argentine President Mauricio Macri has been replaced by the face of Adolf Hitler, and Angela Merkel's face has been replaced with Donald Trump's. 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 January 2019, Fox television affiliate KCPQ aired a deepfake of Trump during his Oval Office address, mocking his appearance and skin color.
In May 2019, speaker of the United States House of Representatives Nancy Pelosi was the subject of two viral videos, one of which had the speed slowed down to 75 percent, and another which edited together parts of her speech at a news conference for the Fox News segment Lou Dobbs Tonight. Both videos were intended to make Pelosi appear as though she was slurring her speech. President Donald Trump shared the latter video on Twitter, captioning the video "'PELOSI STAMMERS THROUGH NEWS CONFERENCE'". These videos were featured by many major news outlets, which brought deepfakes to the attention of the United States House Intelligence Committee.
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. Amateur deepfake technology has already been used 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 in Rogue One.
Degradation of womenEdit
It has been claimed that the original purpose of the technology is to “control and humiliate women.”
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. The perpetrator reportedly called three times and requested a second payment but was turned down when the CEO realized the phone number of the caller was Austrian and that the money was not being reimbursed as he was told it would be.
Effects on credibility and authenticityEdit
The presence of deepfakes makes classifying videos as satirical or genuine increasingly difficult. 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. 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 predicts that genuine videos and deepfakes will become indistinguishable in as soon as half a year, as of October 2019, due to rapid advancement in artificial intelligence and computer graphics.
Most of the academic research surrounding Deepfake seeks to detect the videos. The most popular technique is to use algorithms similar to the ones used to build the deepfake to detect them. By recognizing patterns in how Deepfakes are created the algorithm is able to pick up subtle inconsistencies. Researchers have developed automatic systems that examine videos for errors such as irregular blinking patterns of lighting. This technique has also been criticized for creating a "Moving Goal post" where anytime the algorithms for detecting get better, so do the Deepfakes. The Deepfake Detection Challenge, hosted by a coalition of leading tech companies, hope to accelerate the technology for identifying manipulated content.
Other techniques uses 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.
Scarlett Johansson, a frequent subject of deepfake porn, spoke publicly about the subject to The Washington Post in December 2018. In a prepared statement, she expressed that despite concerns, she would not attempt to remove any of her deepfakes, due to her belief that they do not affect her public image and that differing laws across countries and the nature of internet culture make any attempt to remove the deepfakes "a lost cause". While celebrities like herself are protected by their fame, however, she believes that deepfakes pose a grave threat to women of lesser prominence who could have their reputations damaged by depiction in involuntary deepfake pornography or revenge porn.
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.
In the same month, representatives from Twitter stated that they would suspend accounts suspected of posting non-consensual deepfake content. GIF hosting site Gfycat and chat site Discord are also planning to ban deepfake content from their platforms. On Reddit, the r/deepfakes subreddit was banned on February 7, 2018, due to the policy violation of "involuntary pornography". 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.
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 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 October 3, 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 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 popular cultureEdit
"Picaper" by Jack WodhamsEdit
The 1986 Mid-December issue of Analog 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.
Jack Wodhams calls such fabricated videos picaper or mimepic—image animation. To Wodhams, pornography is not the major danger of this technology. The sobering conclusion is that "the old idea that pictures do not lie is going to have to undergo drastic revision".
A Philosophical InvestigationEdit
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 the female police lieutenant Isadora "Jake" Jakowicz assigned to catch him.
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.
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