[Congressional Record Volume 166, Number 207 (Tuesday, December 8, 2020)]
[House]
[Pages H7022-H7023]
From the Congressional Record Online through the Government Publishing Office [www.gpo.gov]




       IDENTIFYING OUTPUTS OF GENERATIVE ADVERSARIAL NETWORKS ACT

  Mr. TONKO. Mr. Speaker, I ask unanimous consent to take from the 
Speaker's table the bill (S. 2904) 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, and ask for its immediate consideration 
in the House.
  The Clerk read the title of the bill.
  The SPEAKER pro tempore (Mr. Pappas). Is there objection to the 
request of the gentleman from New York?
  There was no objection.
  The text of the bill is as follows:

                                S. 2904

       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

[[Page H7023]]

     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.
  The bill was ordered to be read a third time, was read the third 
time, and passed, and a motion to reconsider was laid on the table.

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