Deepfake Misinformation

Deepfake is a form of media that has been altered by machine learning and artificial intelligence. It is often represented as videos or images that have been manipulated to make it appear as if a person is saying or doing something that they did not in reality actually say or do. The modified media produced can be misleading as the generated visual and audio content is not based on the original material, but rather fake content. Within recent years, the technology behind deepfakes has advanced rapidly, making it more accessible to create realistic fake videos and images. Misinformation, defined as the false or inaccurate information with the sole intention to deceive, has been a key topic in online media with the rise of technology. This growth of the technology has led to many concerns about the potential of deepfakes being used to spread misinformation and propaganda and also includes harassment and blackmailing.

Image of an individual without modifications vs with a deepfake of actor Tom Cruise. Source: https://www.trymaverick.com/blog-posts/are-deep-fakes-all-evil-when-can-they-be-used-for-good

History

Origins

The idea of photo manipulation started in the 19th century , and the general idea of media manipulation started to rise as technology advanced which included video formats. However, using computer algorithms to manipulate media is a newer idea that started in 1997 with the video rewrite program. This program had the ability to put false audio over a video which allowed the manipulator to make a person in the video say anything. However there would be only a small amount of academic research done on this subject up until 2017 which would be the year where the term “deepfake” was used to describe the altered media content.

Modern Use Cases

The term "deepfake" originated from a Reddit user "deepfakes" and the eventual community on Reddit "r/deepfakes". In this community, there are many postings of videos that included celebrities' faces swapped onto the bodies of people in pornographic videos as well as non-pornographic videos such as videos with actor Nicolas Cage's face swapped into various video clips. Currently, there are still many communities online that share both pornographic and non-pornographic deepfake media.

Another modern development of deepfake use is commercially. Many companies have started to use deepfake as a method to create personalized corporate videos as well as using audio deepfake to clone human voices. An example of the commercial use of deepfake is “FakeApp”: a desktop application launched in 2018. The application provides users to create videos that swap faces with other people; users are then able to share these videos. Deepfake is the core technology behind the face swapping and many other applications use deepfake to support their product.

How Deepfake Works

Visual representation of how autoencoders work. Source: https://www.alanzucconi.com/2018/03/14/understanding-the-technology-behind-deepfakes/

The most common technique to create deepfake software is to use machine learning algorithms to generate the altered content based on a large amount of data input. Primarily these techniques are based on autoencoders and on generative adversarial networks (GAN). Autoencoders are a group of self-supervised neural networks that learn to copy their own input. An autoencoder consists of 3 components: an encoder, a code, and a decoder. The encoder compresses the input data and produces the code after the decoder reconstructs the input based only on the code. Another technique that is often used through deepfake is the generative adversarial networks (GAN), which also learns from input data in order to generate new data. The system is trained by two distinctive neural networks: a generator and a discriminator where the generator discovers regularities or patterns in the input dataset and learns to reproduce them. This generated data is used by the discriminator to compare to the real data and check if it can distinguish between the two. This system is trained until the discriminator no longer confuses the generated data with the real data .

How Deepfake is Used to Spread Misinformation

Political Public Figures

Deepfake is uniquely effective at spreading misinformation especially within the setting of democratic discourse. Deepfake videos depicting public figures making incendiary comments or behaving inappropriately may alter election outcomes. In addition, on the reverse, as deepfakes become more well known, public officials caught on camera can also exploit this and claim that a real video is a deepfake. Without clear methods to distinguish what is real from what is not real, the public may lose trust in the media and other public institutions.

An example of how deepfake has deceived high level politicians is the case of when parliamentarians from the UK, Latvia, Lithuania, and Estonia all fell victim to a digital fake: Leonid Volkov, chief of staff to imprisoned Russian anti-Putin politician Alexei Navalny. The culprits were two Russian men who had also many other incidence with deepfake where they had tricked their way into many various meetings with European politicians and even a live interview on Latvian TV. This was done by cold-calling and emailing their targets from fake addresses, using a real picture of Volkov as proof for their “identity”. This incident has brought up concerns about deepfake and the chair of Latvia’s Foreign Affairs Committee stated that “disinformation operations with the use of manipulated and artificial intelligence (AI) generated media were carried to deceive officials" and that it is "encouraged to be vigilant of deepfake" videos.

Pornography

Women have been the largest target for deepfake use. Sensity AI, a research company that has tracked online deepfake videos since December of 2018, has consistently found that between 90% and 95% of them are nonconsensual porn and about 90% of that is nonconsensual porn of only women. In large part, these are released as revenge porn: real intimate photos released without consent. This is particularly harmful as the photos and videos are not only released to the public, but the actual content is also inaccurate.

Although victims have often been of celebrities, it has become more common for non-celebrities to also be targeted of deepfake pornography. In many cases, efforts to silence and harm women by co-opting their sexual identities have led to the use of deepfake videos. An example of this is the story of Noelle Martin, a young woman in Australia who has been advocating about the issue of image-based sexual abuse, also became the subject of manufactured sexual images and deepfaked video. Another incident is about Rana Ayyub, a journalist in India who spoke out against the government’s response to the rape of an eight-year-old girl; she was the subject of a deepfake video made as part of a coordinated online hate campaign.

General News

Another concern is that deepfakes could be used to create fake news stories or other types of misinformation that are spread through social media and other online platforms. These can be used to spread rumors or false information about individuals or groups, potentially causing harm to the reputation of those involved. In addition, this could lead to confusion and mistrust among the public, as well as potentially dangerous consequences such as stock market fluctuations or even violence. Conspiracy theories are also potentially using deepfake to provide proof, which can be dangerous to the public's understanding of general events. In general, deepfake content can be used to trick people by replacing concrete videos or images and posing as the real content as evidence or proof.

Ethical Concerns

Disinformation and Propaganda

The misuse of deepfake can be detrimental as it can be used to spread propaganda to groups of individuals. This could lead to confusion and mistrust among the public, and could even be used to influence elections or sway public opinion on important issues. Additionally, deepfakes can enable the least democratic and authoritarian leaders to thrive as they can leverage the ‘liar’s dividend’, where any inconvenient truth is quickly discounted as ‘fake news’. Deepfakes and synthetic media may have a large impact on the outcome of elections. This disinformation creates large amounts of harm to individuals because it prevents their ability to make informed decisions in their own best interests. Intentionally distributing false information about the opposition or presenting an alternate truth for a candidate in an election manipulates voters into serving the interests of the deceiver. The ethics of deepfake is questioned as they can be used with the intent to deceive, intimidate, and inflict reputational harm to extend disinformation.

Blackmail and Revenge Porn

Deepfake has the potential to be used as blackmail in situations where individuals can threaten to release fake content in hopes of gaining money or other motives. In addition, deepfake porn can be also used to blackmail people (mostly females) as they don’t want to lose their reputation and image. The major issue can be attributed to mimicking a person’s likeness without their permission and in the case of deepfake porn, this can be considered unethical since the victim of the deepfake is being used as a source of pleasure and entertainment, without consent. Additionally, the use of non consensual deepfake can be particularly harmful if someone’s likeness is used without their knowledge.

Scamming

Scams are dishonest schemes and/or frauds used against people mainly for monetary gain. A major issue that deepfake may be used to support are scams and general fraud pursuits. An example would be the use of deepfake to create videos or images of fake reviews. These fake reviews may portray a deepfake of a credible person that vouches for a product; this would be the wrongful impersonation of someone and can pose an issue that deepfake perpetuates. Furthermore, this wrongful impersonation could also be used to elevate a brand’s storytelling which could also pose ethical issues that deepfake encourages.

False accusations and complaints against large companies may also be increased using deepfake. To mislead viewers and the general public, recordings of an actual incident and altering the audio/video with new dialogue or clips can be used. An example of this is a German energy firm’s U.K. subsidiary handed around £220,000 to a Hungarian bank account after a swindler used the power of deepfakes to synthesise the CEO’s voice.

Spotting Deepfakes

Image Detection

With the increased use of deepfake and the large concerns behind them, spotting a deepfake video or picture becomes more important to avoid being deceived. In terms of deepfake image detection, there has been research done on using deep networks and neural network-based methods for detecting fake GAN images. The model first uses deep learning networks to extract face features on face recognition networks; then a calibration procedure is used to make face features suitable for real/fake image detection. Furthermore, other than the traditional deepfake detection models, there is a hybrid method that can also detect fake images: pairwise-learning for deepfake image detection. The technique uses GANs to create and generate a fake image; after, the popular fake feature network (CFFN) generated by GANs, the pairwise-learning model, is used to capture the discriminant information between the fake image and the real image.

Other than using technology to detect deepfake images, there are other ways and cues to spot deceiving images without the support of other technology. One aspect to look for is mismatches in color and lighting. Examples are unusual skin tones, stains, strange lighting, and oddly positioned shadows indicate that what you see might be a deepfake.

Video Detection

In comparison to image deepfake detection, current deep learning deep learning methods for image cannot be directly applied for fake videos detection due to the availability of significant loss of frame information after video compression. However, there are two specific strategies to detect deepfake videos: biological singles analysis and spatial and temporal features analysis.

Biological analysis, proposed by Yuezun Li, presented a new approach based on natural network to detect Fake Face Videos. The basis of this approach is to look at eye blinking to detect fake videos, which is an important physical feature that can be used to distinguish the deepfake videos from real videos. In addition, another aspect is the close relationship between various audio-visual modalities of the same sample. There is a detection method where the primary goal of this model is to comprehend and examine the interaction of the audio (speech) and video (visual) modalities; using this, deepfake videos can be found.

There are also many different methods to spot a deepfake without using technology. Unnatural eye movements such as lack of eye movement and no blinking are clear cues that a video may be deepfake. Strange body shape or movements are also indications such as if a person appears distorted or different when they turn to the side and move their head or if their movements are choppy and disconnected from one frame to the next. Because deepfake technology is mostly concentrated on distorting facial features, anomalies and odd body shapes on the torso are easy to spot as deepfake videos.

Potential Benefits Of Deepfake

Video Campaigns

Although there are many concerns of the harms that deepfake can cause to society, there are also some potential benefits that deepfake technology offers. An alternative low-cost option for marketers is to use deepfake for their videos. Deepfake can help because they can save budget money as they don’t need an in-person actor. Rather than an actor, a marketer can purchase a license for an actor’s identity. With the license, marketers can then use previous digital recordings of the actor, insert the appropriate dialogue from a script for the actor and create a new video. In addition, another application that marketers can use with deepfake is improved omni-channel campaigns. Because in-person actors are not needed for a campaign, existing content can be repurposed for various marketing channels for both less time and less money. Rather than reshooting to fit different purposes for different channels, video cuts can be edited or replaced to create a paid social campaign.

Education

Teachers can take advantage of deepfake technology to deliver meaningful lessons. Historical figures can be imitated in videos with the animation of choice using deepfake; these personalized videos may make lessons more engaging and interactive. These videos could increase engagement and be a more effective learning tool. With the scale and low cost, the use of deepfake synthetic voice and video can also improve success and learning outcomes.

Art

There are many different applications of deepfake in various forms of art. One example is that there is the potential to democratize expensive VFX technology as deepfake can supplement and even mimic the technology. Video game development is another example of how deepfake can be used to innovate. Nvidia has done research and demonstrated that AI-generated graphics and imagery can speed up game development as well as create a hybrid gaming environment. Furthermore, storytelling and audiobooks can be innovated as well with deepfake. Synthetic voices using deepfake can be included now for audio formats of an author’s book with the author’s own voice. Moreover, businesses can broaden the reach of their content by using synthetic voice-overs of the same actor in different languages.