The following is the June 3, 2022 Congressional Research Service In Focus report, Deep Fakes and National Security. "Deep fakes"--a term that first emerged in 2017 to describe realistic photo, audio, video, and other forgeries generated with artificial intelligence (AI) technologies--could present a variety of national security challenges in the years to come. As these technologies continue to mature, they could hold significant implications for congressional oversight, U.S. defense authorizations and appropriations, and the regulation of social media platforms. How Are Deep Fakes Created? Though definitions vary, deep fakes are most commonly described as forgeries created using techniques in machine learning (ML)--a subfield of AI--especially generative adversarial networks (GANs).

"Deep fakes"--a term that first emerged in 2017 to describe realistic photo, audio, video, and other forgeries generated with artificial intelligence (AI) technologies--could present a variety of national security challenges in the years to come. As these technologies continue to mature, they could hold significant implications for congressional oversight, U.S. defense authorizations and appropriations, and the regulation of social media platforms. Though definitions vary, deep fakes are most commonly described as forgeries created using techniques in machine learning (ML)--a subfield of AI--especially generative adversarial networks (GANs). In the GAN process, two ML systems called neural networks are trained in competition with each other. The first network, or the generator, is tasked with creating counterfeit data--such as photos, audio recordings, or video footage--that replicate the properties of the original data set.