    [United States Statutes at Large, Volume 134, 116th Congress, 2nd Session]
    [From the U.S. Government Publishing Office, www.gpo.gov]

    Public Law 116-258
    116th Congress

    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.

    [[Page 1151]]

    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

    [[Page 1152]]

    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.

    Approved December 23, 2020.

    LEGISLATIVE HISTORY--S. 2904 (H.R. 4355):
    ---------------------------------------------------------------------------

    HOUSE REPORTS: No. 116-268 (Comm. on Science, Space, and Technology)
    accompanying H.R. 4355.
    SENATE REPORTS: No. 116-289 (Comm. on Commerce, Science, and
    Transportation).
    CONGRESSIONAL RECORD, Vol. 166 (2020):
    Nov. 18, considered and passed Senate.
    Dec. 8, considered and passed House.