Deepfake

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). 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. 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. 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 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. In cinema studies, deepfakes demonstrate how "the human face is emerging as a central object of ambivalence in the digital age".