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[Congressional Bills 116th Congress]
[From the U.S. Government Publishing Office]
[H.R. 4355 Reported in House (RH)]

<DOC>

                                                 Union Calendar No. 213
116th CONGRESS
  1st Session
                                H. R. 4355

                          [Report No. 116-268]

 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.

_______________________________________________________________________

                    IN THE HOUSE OF REPRESENTATIVES

                           September 17, 2019

Mr. Gonzalez of Ohio (for himself, Ms. Stevens, Mr. Baird, and Ms. Hill 
of California) introduced the following bill; which was referred to the 
              Committee on Science, Space, and Technology

                            November 5, 2019

 Additional sponsors: Ms. Sherrill, Ms. Wexton, Miss Gonzalez-Colon of 
        Puerto Rico, Ms. Slotkin, Mr. Lucas, and Ms. Spanberger
  Reported with an amendment, committed to the Committee of the Whole 
       House on the State of the Union, and ordered to be printed
 [Strike out all after the enacting clause and insert the part printed 
                               in italic]
    [For text of introduced bill, see copy of bill as introduced on 
                          September 17, 2019]

_______________________________________________________________________

                                 A BILL

 
 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) Research gaps currently exist on the underlying 
        technology needed to develop tools to identify authentic 
        videos, voice reproduction, or photos from manipulated or 
        synthesized content, including those generated by generative 
        adversarial networks.
            (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'', in 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 and 
        other comparable 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 the ethics of the technology 
        and 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 Networking and Information 
        Technology Research and Development Program, the Defense 
        Advanced Research Projects Agency and the Intelligence Advanced 
        Research Projects Agency.

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 one year after the date of the 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 Space, Science, and Technology of the House 
of Representatives and the Committee on Commerce, Science, and 
Transportation 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.
                                                 Union Calendar No. 213

116th CONGRESS

  1st Session

                               H. R. 4355

                          [Report No. 116-268]

_______________________________________________________________________

                                 A BILL

 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.

_______________________________________________________________________

                            November 5, 2019

  Reported with an amendment, committed to the Committee of the Whole 
       House on the State of the Union, and ordered to be printed
```