Deepfake

Deepfakes were originally defined as synthetic media that have been digitally manipulated to replace one person's likeness convincingly with that of another. The term was coined in 2017 by a Reddit user, and has later been expanded to cover any videos, pictures, or audio made with artificial intelligence to appear real, for example realistic-looking images of people who do not exist. While the act of creating fake content is not new, deepfakes leverage tools and techniques from machine learning and artificial intelligence, including facial recognition algorithms and artificial neural networks such as variational autoencoders (VAEs) and 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. Both the information technology industry and government have responded with recommendations to detect and limit their use.