Deepfakes (a portmanteau of 'deep learning' and 'fake') are images, videos, or audio that have been edited or generated using artificial intelligence, AI-based tools or audio-video editing software. They may depict real or fictional people and are considered a form of synthetic media, that is media that is usually created by artificial intelligence systems by combining various media elements into a new media artifact.

While the act of creating fake content is not new, deepfakes uniquely leverage machine learning and artificial intelligence techniques, 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 has worked to develop 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.

Academics have raised concerns about the potential for deepfakes to promote disinformation and hate speech, as well as interfere with elections. In response, the information technology industry and governments have proposed recommendations and methods to detect and mitigate their use. Academic research has also delved deeper into the factors driving deepfake engagement online as well as potential countermeasures to malicious application of deepfakes.

From traditional entertainment to gaming, deepfake technology has evolved to be increasingly convincing and available to the public, allowing for 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, aesthetic implications as well as journalistic and informational implications of deepfakes. As deepfakes have risen in prominence in popularity with innovations provided by AI tools, significant research has gone into detection methods and defining the factors driving engagement with deepfakes on the internet. Deepfakes have been shown to appear on social media platforms and other parts of the internet for purposes ranging from entertainment and education related to deepfakes to misinformation to elicit strong reactions. There are gaps in research related to the propagation of deepfakes on social media. Negativity and emotional response are the primary driving factors for users sharing deepfakes.

Age and lack of literacy related to deepfakes are another factor that drives engagement. Older users who may be technologically-illiterate might not recognize deepfakes as falsified content and share this content because they believe it to be true. Alternatively, younger users accustomed to the entertainment value of deepfakes are more likely to share them with an awareness of their falsified content. Despite cognitive ability being a factor in successfully detecting deepfakes, individuals who are aware of a deepfake may be just as likely to share it on social media as one who does not know it is a deepfake. Within scholarship focused on detecting deepfakes, deep-learning methods using techniques to identify software-induced artifacts have been found to be the most effective in separating a deepfake from an authentic product. Due to the capabilities of deepfakes, concerns have developed related to regulations and literacy toward the technology. The potential malicious applications of deepfakes and their capability to impact public figures, reputations, or promote misleading narratives are the primary drivers of these concerns. Amongst some experts, potential malicious applications of deepfakes have encouraged them into labeling deepfakes as a potential danger to democratic societies that would benefit from a regulatory framework to mitigate potential risks.