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 faking content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content with a high potential to 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 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.
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 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.
Researchers have also shown that deepfakes are expanding into other domains such as tampering 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 of the amount of training data 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, artifact 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.
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 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 television 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.
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 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.
Many deepfakes on the internet feature pornography of people, often female celebrities whose likeness is typically used without their consent. Deepfake pornography prominently surfaced on the Internet in 2017, particularly on Reddit. A report published in October 2019 by Dutch cybersecurity startup Deeptrace estimated that 96% of all deepfakes online were pornographic. The first one that captured attention was the Daisy Ridley deepfake, which was featured in several articles. Other prominent pornographic deepfakes were of various other celebrities. 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 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 27 June 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 affiliate KCPQ aired a deepfake of Trump during his Oval Office address, mocking his appearance and skin color (and subsequently fired an employee found responsible for the video).
- 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 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.
In March 2018 the multidisciplinary artist Joseph Ayerle published the videoartwork Un'emozione per sempre 2.0 (English title: The Italian Game). The artist worked with Deepfake technology to create 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 “Creative 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 Artificial Intelligence replaced Jenner’s face by an AI calculated face of Ornella Muti. As a result the cyber personality has the face of the Italian actress Ornella Muti and the body of Kendall Jenner.
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 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.
In 2020, an internet meme emerged utilizing deepfakes to generate videos of people singing the chorus of "Baka Mitai" (ばかみたい), a song from the video game series Yakuza. 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 Dobbysrules, who lip syncs the song, as a template.
Deepfake photographs can be used to create sockpuppets, non-existent persons, 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 British legal academic Mazen Masri and his wife, Palestinian human rights defender Ryvka Barnard, as "terrorist symphathizers." Masri 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 "his" photo is a deepfake. Several newspapers have not retracted "his" articles 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.
Credibility and authenticity
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. 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 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.
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.
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 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. There are multiple ways for citizens in Canada to deal with deepfakes if they are targeted by them.
In popular culture
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