[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.