Deepfakes (portmanteau of "deep learning" and "fake") are synthetic media that have been digitally manipulated to replace one person's likeness convincingly with that of another. Deepfakes are the manipulation of facial appearance through deep generative methods. While the act of creating fake content is not new, deepfakes leverage powerful techniques from machine learning and artificial intelligence to manipulate or generate visual and audio content that can more easily 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). In turn the field of image forensics develops techniques to detect manipulated images.
Deepfakes have garnered widespread attention for their potential use in creating child sexual abuse material, celebrity pornographic videos, revenge porn, fake news, hoaxes, bullying, and financial fraud. The spreading of disinformation and hate speech through deepfakes has a potential to undermine core functions and norms of democratic systems by interfering with people's ability to participate in decisions that affect them, determine collective agendas and express political will through informed decision-making. This has elicited responses from both industry and government to detect and limit their use.
From traditional entertainment to gaming, deepfake technology has evolved to be increasingly convincing and available to the public, allowing the disruption of the entertainment and media industries.
History
Photo manipulation was developed in the 19th century and soon applied to motion pictures. Technology steadily improved during the 20th century, and more quickly with the advent of digital video.
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
Academic research related to deepfakes is split between the field of computer vision, a sub-field of computer science, which develops techniques for creating and identifying deepfakes, and humanities and social science approaches that study the social, ethical and aesthetic implications of deepfakes.
Social science and humanities approaches to deepfakes
In cinema studies, deepfakes demonstrate how "the human face is emerging as a central object of ambivalence in the digital age". Video artists have used deepfakes to "playfully rewrite film history by retrofitting canonical cinema with new star performers". Film scholar Christopher Holliday analyses how switching out the gender and race of performers in familiar movie scenes destabilizes gender classifications and categories. The idea of "queering" deepfakes is also discussed in Oliver M. Gingrich's discussion of media artworks that use deepfakes to reframe gender, including British artist Jake Elwes' Zizi: Queering the Dataset, an artwork that uses deepfakes of drag queens to intentionally play with gender. The aesthetic potentials of deepfakes are also beginning to be explored. Theatre historian John Fletcher notes that early demonstrations of deepfakes are presented as performances, and situates these in the context of theater, discussing "some of the more troubling paradigm shifts" that deepfakes represent as a performance genre.
Philosophers and media scholars have discussed the ethics of deepfakes especially in relation to pornography. Media scholar Emily van der Nagel draws upon research in photography studies on manipulated images to discuss verification systems that allow women to consent to uses of their images.
Beyond pornography, deepfakes have been framed by philosophers as an "epistemic threat" to knowledge and thus to society. There are several other suggestions for how to deal with the risks deepfakes give rise beyond pornography, but also to corporations, politicians and others, of "exploitation, intimidation, and personal sabotage", and there are several scholarly discussions of potential legal and regulatory responses both in legal studies and media studies. In psychology and media studies, scholars discuss the effects of disinformation that uses deepfakes, and the social impact of deepfakes.