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. Film scholar Christopher Holliday analyses how switching out the gender and race of performers in familiar movie scenes destabilises 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 subfield 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. This does not affect your statutory rights.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. uk accepts no liability for inaccuracies or misstatements about products by manufacturers or other third parties. Information and statements about products are not intended to be used to diagnose, treat, cure, or prevent any disease or health condition. Contact your health-care provider immediately if you suspect that you have a medical problem. Content on this site is not intended to substitute for advice given by medical practitioner, pharmacist, or other licensed health-care professional. In the event of any safety concerns or for any other information about a product please carefully read any instructions provided on the label or packaging and contact the manufacturer. Please always read the labels, warnings, and directions provided with the product before using or consuming a product. We recommend that you do not solely rely on the information presented on our website. All information about the products on our website is provided for information purposes only. Actual product packaging and materials may contain more and/or different information than that shown on our website. Disclaimer: While we work to ensure that product information on our website is correct, on occasion manufacturers may alter their ingredient lists.
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