In March 2023, a footage was shared on the Russian social networking site VKontakte of President Zelensky announcing that Ukraine would surrender to Russian invasion. The video also started to spread on Facebook and YouTube. Shortly after the video went viral, the real President denied the video on his social media page, and it was quickly removed by the social media sites. The video was a low-quality deepfake, the President’s likeness looked unnatural, his face did not match his body and his voice was different from the person the video was based on (the target). Thanks to this and the quick reaction from him, the fake video footage did not cause much damage. This case is just one alarming example of the many instances of artificial intelligence-generated or manipulated content that have been released, often causing widespread panic.
But what is a deepfake? A media content in which a person’s features are altered by Artificial Intelligence to make them look like someone else. The term is a combination of the terms “deep learning” and “fake“. So, the creator deliberately wants to deceive the recipient of the multimedia and does it by using machine learning and Artificial Intelligence. Therefore, not all audiovisual content manipulated by artificial intelligence falls into this category. AI-based manipulation is also used legally, for example in the film industry, by using large amounts of voice recordings of an actor – living or deceased – to read out texts in their voice.
The technology has (over)developed in recent years. Although the theoretical foundations of it were laid in an academic paper in the 1990s, it entered the public domain in 2017 when an anonymous user created a deepfake algorithm using existing algorithms and made it available for free on the internet. The initial videos were astonishing, but it was obvious to anyone that something was wrong with the footage, that it had been manipulated. The time it took to make the footage, which initially took a week of post-production, was reduced to a few days, and soon software appeared that allowed real-time conversion. One of the best-known and most authentic deepfakes is the TikTok channel Deep Tom Cruise, created by an actor who looks a lot like Tom Cruise. The channel produces videos for entertainment purposes, not for deception. However, it draws our attention to the fact that it is possible to create a completely deceptive result from a combination of an actor who resembles the target person from the ground up and a lot of sophisticated post-production work.
In the following, it will be presented what must be achieved first and foremost to ensure that the use of deepfake technologies does not lead to practices that adversely affect society and violate fundamental rights.
A major upgrade of deepfake-detecting technology is inevitable
The most “primitive” of deepfake detection technologies is the free-eye monitoring. Low-quality video content can be spotted after a close and thorough examination. The most common clues are unnatural eye and mouth movements, speech being out of sync, unnatural facial twitches, uneven skin tone, etc. However, these signs are only easily noticeable if the viewer is aware that the technology exists. Someone who has no knowledge of deepfakes at all will not even think to look closely at these typical signs. Also, in response to deepfake videos, artificial intelligence technologies have emerged to detect them. There are several paid and free services available on the internet that can tell in a matter of seconds whether an uploaded video is a deepfake or not. Most of them are highly accurate and aggregate the results of several detection algorithms. The problem is therefore not so much the effectiveness of this software, but the fact that the average user has no such tools at his disposal or is not even aware of their existence. Filtering should therefore be implemented first and foremost by incorporating it into the filtering mechanisms of social networking sites and addressing the problem at its root. For example, Facebook has announced a competition for detection algorithms in 2020. Competitors had to work with a database of over 100,000 samples. The most accurate algorithm was able to filter out fake photos with 82% accuracy. Unfortunately, the figure is not reassuringly high, leaving a large margin for higher-quality fakes. Several other well-known social networking sites have also announced changes to their community guidelines. TikTok banned “synthetic media” in April this year. It is only possible to upload fake videos to the platform if they are clearly labeled “synthetic”, “fake”, “not real” or “altered”.
Deepfake-specific regulation is needed
The widespread use and increasing realism of deepfake technology requires urgent and thorough legal regulation. It is not possible, nor would it be reasonable, to ban the technology outright, as it has many useful and creative uses. It is widely applied by the film industry and can also be used to produce entertaining, humorous videos that do not infringe the law. However, there is a need to regulate the technology specifically. In the US, three states have passed legislation. Virginia focuses on pornographic deepfakes, while Texas and California on disinformation aimed at influencing election results. Also, several federal initiatives have been taken but have not yet been adopted. The United Kingdom has already indicated its intention to introduce a law banning the unauthorized use of pornographic deepfakes. China has already enacted a deepfake-specific regulation, which is currently the most detailed and stringent deepfake legislation in the world. The law prohibits anyone from creating deepfake content without the subject’s permission and from depicting or saying anything that could be considered contrary to national interests. Anything that is contrary to socialist values is included, as is any form of “illegal and harmful information” or the use of AI-generated human imagery to deceive or defame. The law is part of China’s strategy to become a world leader in comprehensive technology regulation.
Special rules are needed for electoral procedures
Deepfake content can be particularly dangerous during election campaigns. The use of a deepfake video recording in the run-up to election day can bring in a lot of votes for the abusive party and can be particularly helpful in convincing undecided voters. Usually, deepfakes are identified with the videos, but by means of fake audio recordings an even more dangerous use can be made. This is because it is more difficult to prove where and when a so-called ‘leaked’ audio recording was made. One can find several parodies made for entertainment purposes that include dialogue between current and former US presidents – and they are very convincing fakes. On this basis, there are just enough audio recordings available of a politician who is less significant globally but crucial to the outcome of the election in a smaller country. And since the election campaigns of recent years, both at home and abroad, have shown that politicians are willing to use any unscrupulous means to win, there is no reason why they could not use the persuasive power of deepfake technology.
The three main changes that should be implemented – enhanced use of deepfake-detecting technology, deepfake-specific regulation, and special rules for electoral procedures – are essential to ensure that deepfakes do not have an irreversibly damaging impact on our society. There are risks involved in the use of this technology in several areas, such as data protection and privacy rights, the potential for manipulated content to have a detrimental impact on the political process and public opinion, and even the difficulty of proving the credibility of evidence used in legal proceedings. Appropriate legislation should therefore aim to set the framework for ethical use. For deepfakes, as for any emerging technology, it is important that the legal framework keeps pace with the rapid evolution of the technology.
Gellért MAGONY is a student at the Faculty of Law and Political Sciences of the University of Szeged, Hungary, and a scholarship student of the Aurum Foundation. His main area of interest is the relationship between the digital world and law. His previous research has focused on the relationship between social networking sites and freedom of expression and the statehood of metaverses. He is currently researching social influence through deepfakes.