[Congressional Bills 116th Congress]
[From the U.S. Government Publishing Office]
[S. 2904 Enrolled Bill (ENR)]
S.2904
One Hundred Sixteenth Congress
of the
United States of America
AT THE SECOND SESSION
Begun and held at the City of Washington on Friday,
the third day of January, two thousand and twenty
An Act
To direct the Director of the National Science Foundation to support
research on the outputs that may be generated by generative adversarial
networks, otherwise known as deepfakes, and other comparable techniques
that may be developed in the future, and for other purposes.
Be it enacted by the Senate and House of Representatives of the
United States of America in Congress assembled,
SECTION 1. SHORT TITLE.
This Act may be cited as the ``Identifying Outputs of Generative
Adversarial Networks Act'' or the ``IOGAN Act''.
SEC. 2. FINDINGS.
Congress finds the following:
(1) Gaps currently exist on the underlying research needed to
develop tools that detect videos, audio files, or photos that have
manipulated or synthesized content, including those generated by
generative adversarial networks. Research on digital forensics is
also needed to identify, preserve, recover, and analyze the
provenance of digital artifacts.
(2) The National Science Foundation's focus to support research
in artificial intelligence through computer and information science
and engineering, cognitive science and psychology, economics and
game theory, control theory, linguistics, mathematics, and
philosophy, is building a better understanding of how new
technologies are shaping the society and economy of the United
States.
(3) The National Science Foundation has identified the ``10 Big
Ideas for NSF Future Investment'' including ``Harnessing the Data
Revolution'' and the ``Future of Work at the Human-Technology
Frontier'', with artificial intelligence is a critical component.
(4) The outputs generated by generative adversarial networks
should be included under the umbrella of research described in
paragraph (3) given the grave national security and societal impact
potential of such networks.
(5) Generative adversarial networks are not likely to be
utilized as the sole technique of artificial intelligence or
machine learning capable of creating credible deepfakes. Other
techniques may be developed in the future to produce similar
outputs.
SEC. 3. NSF SUPPORT OF RESEARCH ON MANIPULATED OR SYNTHESIZED CONTENT
AND INFORMATION SECURITY.
The Director of the National Science Foundation, in consultation
with other relevant Federal agencies, shall support merit-reviewed and
competitively awarded research on manipulated or synthesized content
and information authenticity, which may include--
(1) fundamental research on digital forensic tools or other
technologies for verifying the authenticity of information and
detection of manipulated or synthesized content, including content
generated by generative adversarial networks;
(2) fundamental research on technical tools for identifying
manipulated or synthesized content, such as watermarking systems
for generated media;
(3) social and behavioral research related to manipulated or
synthesized content, including human engagement with the content;
(4) research on public understanding and awareness of
manipulated and synthesized content, including research on best
practices for educating the public to discern authenticity of
digital content; and
(5) research awards coordinated with other federal agencies and
programs, including the Defense Advanced Research Projects Agency
and the Intelligence Advanced Research Projects Agency, with
coordination enabled by the Networking and Information Technology
Research and Development Program.
SEC. 4. NIST SUPPORT FOR RESEARCH AND STANDARDS ON GENERATIVE
ADVERSARIAL NETWORKS.
(a) In General.--The Director of the National Institute of
Standards and Technology shall support research for the development of
measurements and standards necessary to accelerate the development of
the technological tools to examine the function and outputs of
generative adversarial networks or other technologies that synthesize
or manipulate content.
(b) Outreach.--The Director of the National Institute of Standards
and Technology shall conduct outreach--
(1) to receive input from private, public, and academic
stakeholders on fundamental measurements and standards research
necessary to examine the function and outputs of generative
adversarial networks; and
(2) to consider the feasibility of an ongoing public and
private sector engagement to develop voluntary standards for the
function and outputs of generative adversarial networks or other
technologies that synthesize or manipulate content.
SEC. 5. REPORT ON FEASIBILITY OF PUBLIC-PRIVATE PARTNERSHIP TO DETECT
MANIPULATED OR SYNTHESIZED CONTENT.
Not later than 1 year after the date of enactment of this Act, the
Director of the National Science Foundation and the Director of the
National Institute of Standards and Technology shall jointly submit to
the Committee on Science, Space, and Technology of the House of
Representatives, the Subcommittee on Commerce, Justice, Science, and
Related Agencies of the Committee on Appropriations of the House of
Representatives, the Committee on Commerce, Science, and Transportation
of the Senate, and the Subcommittee on Commerce, Justice, Science, and
Related Agencies of the Committee on Appropriations of the Senate a
report containing--
(1) the Directors' findings with respect to the feasibility for
research opportunities with the private sector, including digital
media companies to detect the function and outputs of generative
adversarial networks or other technologies that synthesize or
manipulate content; and
(2) any policy recommendations of the Directors that could
facilitate and improve communication and coordination between the
private sector, the National Science Foundation, and relevant
Federal agencies through the implementation of innovative
approaches to detect digital content produced by generative
adversarial networks or other technologies that synthesize or
manipulate content.
SEC. 6. GENERATIVE ADVERSARIAL NETWORK DEFINED.
In this Act, the term ``generative adversarial network'' means,
with respect to artificial intelligence, the machine learning process
of attempting to cause a generator artificial neural network (referred
to in this paragraph as the ``generator'' and a discriminator
artificial neural network (referred to in this paragraph as a
``discriminator'') to compete against each other to become more
accurate in their function and outputs, through which the generator and
discriminator create a feedback loop, causing the generator to produce
increasingly higher-quality artificial outputs and the discriminator to
increasingly improve in detecting such artificial outputs.
Speaker of the House of Representatives.
Vice President of the United States and
President of the Senate.