Title: IOGAN Act
Policy area: Science, Technology, Communications

Summary:
Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act
This bill directs the National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST) to support research on generative adversarial networks. A generative adversarial network is a software system designed to be trained with authentic inputs (e.g., photographs) to generate similar, but artificial, outputs (e.g., deepfakes).
Specifically, the NSF must support research on the science and ethics of material produced by generative adversarial networks and NIST must support research to accelerate the development of tools to examine the function and outputs of generative adversarial networks.

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

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116th CONGRESS
  1st Session
                                H. R. 4355

 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

_______________________________________________________________________

                                 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 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 FOR OUTPUTS OF GENERATIVE ADVERSARIAL 
              NETWORKS.

    The Director of the National Science Foundation, in consultation 
with other relevant Federal agencies, shall support merit-reviewed and 
competitively awarded research on the science and ethics of material 
produced by generative adversarial networks, which may include--
            (1) supplementing fundamental research on digital media 
        forensic tools or comparable technologies for detection of the 
        outputs of generative adversarial networks completed by the 
        Defense Advanced Research Projects Agency and the Intelligence 
        Advanced Research Projects Activity;
            (2) fundamental research on developing constraint aware 
        generative adversarial networks; and
            (3) social and behavioral research on the ethics of the 
        technology, and human engagement with the networks.

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.
    (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 of 
        generative adversarial networks or to develop constraint aware 
        generative adversarial networks; and
            (2) to consider the feasibility of an ongoing public and 
        private sector engagement to develop voluntary standards for 
        the outputs of generative adversarial networks or comparable 
        technologies.

SEC. 5. REPORT ON FEASIBILITY OF PUBLIC-PRIVATE PARTNERSHIP TO DETECT 
              OUTPUTS OF GENERATIVE ADVERSARIAL NETWORKS AND COMPARABLE 
              TECHNOLOGIES.

    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 Science, Space, 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 outputs of generative 
        adversarial networks or comparable technologies; 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 media products produced by 
        generative adversarial networks or comparable technologies.

SEC. 6. DEFINITIONS.

    In this Act:
            (1) Generative adversarial network.--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.
            (2) Comparable technology.--The term ``comparable 
        technology'' means technology that utilizes similar techniques 
        to achieve the same outputs as a generative adversarial 
        network.
            (3) Constraint aware.--The term ``constraint aware'' means, 
        with respect to artificial intelligence, the generation of 
        realistic relational data by a machine with constraint on the 
        modules generated by an adversarial network.
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