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![handle is hein.crs/govedph0001 and id is 1 raw text is: Deep Fakes and National Security
Updated June 8, 2021
Deep fakes-a termthat first emerged in 2017 to descrbe
realistic photo, audio, video, and other forgeries generated
with artificial intelligence (AI) technologies-could present
a variety ofnational security challenges in the years to
come. As these technologies continue to mature, they could
hold significantimplications for congressionaloversight,
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 Al-especially generative
advers arial networks (GANs). In the GANprocess, two ML
systems called neural networks are trainedin competition
with each other. The first network, or the generator, is
tasked with creating counterfeit data-such as photos, audio
recordings, orvideo footage-thatreplicatethe 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 suchthat the discriminator can nolonger
distinguish between real and counterfeit data.
Though media manipulation is not a new phenomenon, the
use of Alto 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, usingpublically available data, create
increasingly convincing counterfeit content.
How Could Deep Fakes Be Used?
Deep fake technology has beenpopularized for
entertainment purposes-for example, social media users
inserting the actor Nicholas Cage into movies in which he
did not originally appear and a museumgenerating an
interactive exhibit with artist Salvador Dali. Deep fake
technologies have alsobeenusedforbeneficialpurposes.
For example, medical researchers have reported using
GANs to synthesize fake medicalimages to train disease
detection algorithms forrare diseases and to minimize
patient privacy concerns.
Deep fakes could, however, be used for nefarious purposes.
State adversaries orpolitically motivatedindividuals could
release falsified videos of elected officials or otherpublic
figures making incendiary comments orbehaving
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 pres identialelection to undermine public faith in the
U.S. democratic process, denigrate Secretary Clinton, and
harm her electability and potentialpresidency. In the
future, convincing audio or video forgeries could
potentially strengthen similar efforts.
Deep fakes could also be used to embarrass or blackmail
elected officials or individuals with access to classified
information. Already there is evidence that foreign
intelligence operatives have used deep fake photos to create
fake social media accounts fromwhich they have attempted
to recruit sources. Some analysts have suggested thatdeep
fakes could similarly be used to generate inflammatory
content-such as convincing video of U.S. military
personnel eng aged in war crimes-intended to radicalize
populations, recruit terrorists, or incite violence. Section
589F of the FY2021 National Defense Authorization Act
(P.L. 116-283) directs the Secretary of Defense to conduct
an intelligence as sessment of the threat posed by deep fakes
to servicemembers and their families, including an
as s essment of the maturity of the technology and how it
might be used to conduct information operations.
In addition, deep fakes could produce an effect that
profes sors Danielle Keats Citron and Robert Chesney have
termed the Liar's Dividend; it involves the notion that
individuals could successfully deny the authenticity of
genuine content-particularly if it depicts inappropriate or
criminal behavior-by claiming that the content is a deep
fake. Citron and Chesney suggest that the Liar's Dividend
could become more powerful as deep fake technology
proliferates andpublic knowledge of the technology grows.
Some reports indicate that such tactics have already been
used forpoliticalpurposes. Forexample, political
opponents of GabonPresident AliBongo assertedthat a
video intendedto demonstrate his goodhealth and mental
competency was a deep fake, later citing it as part of the
justification for an attempted coup. Outsideexperts were
unable to determine the video's authenticity, but one expert
noted, in some way sit doesn't matter if [the video is] a
fake... It can be usedto justundermine credibility and cast
doubt.
How Can Deep Fakes Be Detected?
Today, deep fakes can often be detected without specialized
detection tools. However, the sophistication of the
technology is rapidly progressing to a point at which
unaided human detection willbe very difficult or
impossible. While commercial industry has been investing
in automated deep fake detection tools, this section
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