#DEEP FAKE VIDEOS VERIFICATION#
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. Philosophers and media scholars have discussed the ethics of deepfakes especially in relation to pornography. 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. The aesthetic potentials of deepfakes are also beginning to be explored. 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 idea of " queering" deepfakes is also discussed in Oliver M.
#DEEP FAKE VIDEOS MOVIE#
Film scholar Christopher Holliday analyses how switching out the gender and race of performers in familiar movie scenes destabilizes gender classifications and categories. Video artists have used deepfakes to "playfully rewrite film history by retrofitting canonical cinema with new star performers". In cinema studies, deepfakes demonstrate how "the human face is emerging as a central object of ambivalence in the digital age". Social science and humanities approaches to deepfakes Academic research Īcademic 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. More recently the methods have been adopted by industry. Technology steadily improved during the 20th century, and more quickly with the advent of digital video.ĭeepfake technology has been developed by researchers at academic institutions beginning in the 1990s, and later by amateurs in online communities. Photo manipulation was developed in the 19th century and soon applied to motion pictures.
įrom 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. This has elicited responses from both industry and government to detect and limit their use. ĭeepfakes 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 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). 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. Deepfakes are the manipulation of facial appearance through deep generative methods. 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.
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