The following is the April 17, 2023, Congressional Research Service report, Deep Fakes and National Security.
From the report
“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). 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. The second network, or the discriminator, is tasked with identifying the counterfeit data. Based on the results of each iteration, the generator network adjusts to create increasingly realistic data. The networks continue to compete—often for thousands or millions of iterations—until the generator improves its performance such that the discriminator can no longer distinguish between real and counterfeit data.
Though media manipulation is not a new phenomenon, the use of AI to generate deep fakes is causing concern because the results are increasingly realistic, rapidly created, and cheaply made with freely available software and the ability to rent processing power through cloud computing. Thus, even unskilled operators could download the requisite software tools and, using publically available data, create increasingly convincing counterfeit content.
How Could Deep Fakes Be Used?
Deep fake technology has been popularized for entertainment purposes—for example, social media users inserting the actor Nicholas Cage into movies in which he did not originally appear and a museum generating an interactive exhibit with artist Salvador Dalí. Deep fake technologies have also been used for beneficial purposes. For example, medical researchers have reported using GANs to synthesize fake medical images to train disease detection algorithms for rare diseases and to minimize patient privacy concerns.
Deep fakes could, however, be used for nefarious purposes. State adversaries or politically motivated individuals could release falsified videos of elected officials or other public figures making incendiary comments or behaving inappropriately. Doing so could, in turn, erode public trust, negatively affect public discourse, or even sway an election. Indeed, the U.S. intelligence community concluded that Russia engaged in extensive influence operations during the 2016 presidential election to “undermine public faith in the U.S. democratic process, denigrate Secretary Clinton, and harm her electability and potential presidency.” Likewise, in March 2022, Ukrainian President Volodymyr Zelensky announced that a video posted to social media—in which he appeared to direct Ukrainian soldiers to surrender to Russian forces—was a deep fake. While experts noted that this deep fake was not particularly sophisticated, in the future, convincing audio or video forgeries could potentially strengthen malicious influence operations.
Download the document here.
