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116th Congress }                                            { Report
                        HOUSE OF REPRESENTATIVES
 1st Session   }                                            { 116-268

======================================================================

 
       IDENTIFYING OUTPUTS OF GENERATIVE ADVERSARIAL NETWORKS ACT

                                _______
                                

November 5, 2019.--Committed to the Committee of the Whole House on the 
              State of the Union and ordered to be printed

                                _______
                                

    Ms. Johnson of Texas, from the Committee on Science, Space, and 
                  Technology, submitted the following

                              R E P O R T

                        [To accompany H.R. 4355]

      [Including cost estimate of the Congressional Budget Office]

    The Committee on Science, Space, and Technology, to whom 
was referred the bill (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, 
having considered the same, report favorably thereon with an 
amendment and recommend that the bill as amended do pass.

                                CONTENTS

                                                                   Page
   I. Amendment.......................................................2
  II. Purpose of the Bill.............................................3
 III. Background and Need for the Legislation.........................3
  IV. Committee Hearings..............................................4
   V. Committee Consideration and Votes...............................4
  VI. Summary of Major Provisions of the Bill.........................4
 VII. Section-By-Section Analysis (By Title and Section)..............5
VIII. Committee Views.................................................6
  IX. Cost Estimate...................................................6
   X. Congressional Budget Office Cost Estimate.......................6
  XI. Compliance with Public Law 104-4 (Unfunded Mandates)............7
 XII. Committee Oversight Findings and Recommendations................7
XIII. Statement on General Performance Goals and Objectives...........7
 XIV. Federal Advisory Committee Statement............................8
  XV. Duplication of Federal Programs.................................8
 XVI. Earmark Identification..........................................8
XVII. Applicability to the Legislative Branch.........................8
XVIII.Statement on Preemption of State, Local, or Tribal Law..........8

 XIX. Changes in Existing Law Made by the Bill, As Reported...........8
  XX. Proceedings of Full Committee Markup............................8

    The amendment is as follows:
  Strike all after the enacting clause and insert the 
following:

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.

                        II. Purpose of the Bill

    The purpose of the bill is to provide for research on 
manipulated or synthesized content and information 
authenticity, including output of generative adversarial 
networks, otherwise known as deepfakes and to encourage public-
private partnerships to develop standards for detecting and 
identifying such content.

              III. Background and Need for the Legislation

    Disinformation in its many forms has long been used by 
governments and rogue organizations and individuals as a weapon 
against adversaries. The problem has become more pervasive in 
the past decade with the explosive growth of social media, 
which provides an opportunity for hostile actors to project 
disinformation directly into the popular discourse at little 
cost.
    Advancements in computing power and the widespread use of 
artificial intelligence over the past several years have made 
it easier and cheaper than ever before to manipulate and 
reproduce photographs and video and audio clips potentially 
harmful or deceptive to the American public and to the 
integrity of our democratic institutions and processes, 
including fake videos featuring ``people'' who do not really 
exist. AI programs can also write convincing articles and blog 
posts that seem to be written by real humans. This technology, 
often referred to as ``deepfake technology'' has developed 
rapidly over the past several years with no clear method of 
identifying and stopping it from becoming a major threat to 
national security, economic security, or public health. The 
ability to identify and label this content is critical to 
preventing bad actors from using manipulated images and videos 
to shift U.S. public opinion. While the deep fake technology 
continues to mature, researchers are only beginning to develop 
the knowledge and tools that will help the public and private 
sector distinguish authentic content from manipulated or 
synthesized content.

                         IV. Committee Hearings

    On September 26, 2019, the Investigations and Oversight 
Subcommittee held a hearing entitled, ``Online Imposters and 
Disinformation.'' The purpose of the hearing was to explore the 
enabling technologies for disinformation online, including deep 
fakes, explore trends and emerging technology in the field, and 
consider research strategies that can help stem the tide of 
malicious inauthentic behavior. The hearing featured a 
demonstration of a deep fake video created using the words and 
video of two Members of Congress.
    Three witnesses testified: Dr. Hany Farid, Professor, 
Electrical Engineering & Computer Science and the School of 
Information, University of California, Berkeley; Dr. Siwei Lyu, 
Professor, Department of Computer Science, Director, Computer 
Vision and Machine Learning Lab, University at Albany, State 
University of New York; and Ms. Camille Francois; Chief 
Innovation Officer, Graphika.

                  V. Committee Consideration and Votes

    On September 17, 2019 Rep. Anthony Gonzalez and Rep. Haley 
Stevens, as well as Rep. Jim Baird and Rep. Katie Hill, 
introduced H.R. 4355, the Identifying Outputs of Generative 
Adversarial Networks Act. The bill was referred to the House 
Science, Space, and Technology Committee.
    On September 25, 2019, the Committee met to consider H.R. 
4355. Mr. Gonzalez offered an amendment in the nature of a 
substitute to make technical corrections and conforming 
changes. The amendment was agreed to by voice vote. Mr. Beyer 
introduced an amendment to the amendment to include fundamental 
research on technical tools for identifying manipulated or 
synthesized content, such as watermarking systems for generated 
media. The amendment was agreed to by voice vote. Ms. Wexton 
introduced an amendment to the amendment to include research on 
public understanding and awareness of manipulated or 
synthesized content, including research on best practices. The 
amendment was agreed to by voice vote. Ms. Johnson moved that 
the Committee favorably report the bill, H.R. 4355, to the 
House with the recommendation that the bill be approved. The 
motion was agreed to by voice vote.

              VI. Summary of Major Provisions of the Bill

    The Act directs the National Science Foundation (NSF) to 
support research on manipulated or synthesized content and 
information security, including fundamental research on digital 
media forensic tools, social and behavioral research, and 
research awards coordinated with other federal agencies and 
programs including NITRD, DARPA and IARPA.
    The Act directs the National Institute of Standards and 
Technology (NIST) to support research for the development of 
measurements and standards necessary to accelerate the 
development of technological tools to examine the function and 
outputs of generative adversarial networks and other 
technologies that synthesize or manipulate content.
    Further the Act directs NSF and NIST to jointly submit to 
Congress a report on the feasibility of and policy 
recommendations for a public-private partnership for research 
to detect manipulated or synthesized content.

        VII. Section-by-Section Analysis (By Title and Section)


Section 1. Short title

    Identifying Outputs of Generative Adversarial Networks Act 
or the IOGAN Act.

Section 2. Findings

    Provides findings for the Act that there are research gaps 
on the underlying technology needed to develop tools to 
identify authentic videos, voice reproduction, or photos and 
those generated by generative adversarial networks (otherwise 
known as ``deepfakes''), and that there is a role for the NSF 
in conducting research on these gaps including social and 
behavioral research.

Section 3. NSF support of research on manipulated or synthesized 
        content and information security

    Directs the National Science Foundation, in consultation 
with other Federal agencies, to conduct research on manipulated 
or synthesized content and information authenticity, including 
fundamental research on digital forensic tools and social and 
behavioral research on the ethics of the technology and human 
engagement with the content.

Section 4. NIST support for research and standards on generative 
        adversarial networks

    Directs the National Institute of Standards and Technology 
to 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 and other technologies that 
synthesize or manipulate content; Directs NIST to solicit input 
from private, public, and academic stakeholders; Directs NIST 
to consider the feasibility of an ongoing public and private 
sector engagement to develop voluntary standards for the 
outputs of generative adversarial networks and other 
technologies.

Section 5. Report on feasibility of public-private partnership to 
        detect manipulated or synthesized content

    Directs NSF and NIST to jointly submit to Congress a report 
on opportunities for research partnerships with the private 
sector on generative adversarial networks or other technologies 
that synthesize or manipulate content.

Section 6. Generative adversarial network defined

    Provides a definition for ``generative adversarial 
network''.

                         VIII. Committee Views

    The intent of this legislation is to accelerate the 
progress of research and the development of measurements, 
standards, and tools to combat manipulated media content, 
including the outputs of generative adversarial networks, 
commonly called ``deepfakes.''
    The Committee recognizes that NSF is already making 
investments in the area of manipulated or synthesized content 
through its Secure and Trustworthy Cyberspace (SaTC) and Robust 
Intelligence (RI) programs. The Committee encourages NSF to 
continue to fund cross-directorate research through these 
programs and others to achieve the purposes of this Act, 
including social and behavioral research on the ethics of these 
technologies and human interaction with the content generated 
by these technologies.
    The Committee intends for NSF and NIST, in carrying out 
this Act, to work with other agencies conducting work on 
detecting manipulated and synthesized content, including DARPA, 
IARPA and the agencies that participate in the NITRD program, 
to ensure coordination and avoid duplication of effort.

                           IX. Cost Estimate

    Pursuant to clause 3(c)(2) of rule XIII of the Rules of the 
House of Representatives, the Committee adopts as its own the 
estimate of new budget authority, entitlement authority, or tax 
expenditures or revenues contained in the cost estimate 
prepared by the Director of the Congressional Budget Office 
pursuant to section 402 of the Congressional Budget Act of 
1974.

              X. Congressional Budget Office Cost Estimate

                                     U.S. Congress,
                               Congressional Budget Office,
                                  Washington, DC, October 29, 2019.
Hon. Eddie Bernice Johnson,
Chairwoman, Committee on Science, Space, and Technology, House of 
        Representatives, Washington, DC.
    Dear Madam Chairwoman: The Congressional Budget Office has 
prepared the enclosed cost estimate for H.R. 4355, the 
Identifying Outputs of Generative Adversarial Networks Act.
    If you wish further details on this estimate, we will be 
pleased to provide them. The CBO staff contact is Janani 
Shankaran.
            Sincerely,
                                         Phillip L. Swagel,
                                                          Director.
    Enclosure.

    
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    H.R. 4355 would require the National Science Foundation 
(NSF) to support research on manipulated digital content and 
information authenticity. The bill also would direct the 
National Institute of Standards and Technology (NIST) to create 
measurements and standards for the development of technological 
tools that examine generative adversarial networks (GANs), 
which are used to produce manipulated content.
    Using information from the NSF, CBO estimates that 
implementing the bill would have no significant cost for the 
NSF because the agency is already carrying out the required 
activities through its existing grant programs. Using 
information from NIST, CBO estimates that the agency would 
require 10 additional employees at an average annual cost of 
$175,000 each over the 2020-2022 period to establish a research 
program on GANs and similar technologies. The bill also would 
direct NIST and the NSF to report to the Congress on related 
policy recommendations. Based on the costs of similar tasks, 
CBO estimates that developing the report would cost less than 
$500,000. In total, CBO estimates that implementing H.R. 4355 
would cost $6 million over the 2020-2024 period; such spending 
would be subject to the availability of appropriated funds.
    The CBO staff contacts for this estimate are Janani 
Shankaran and David Hughes. The estimate was reviewed by H. 
Samuel Papenfuss, Deputy Assistant Director for Budget 
Analysis.

                     XI. Federal Mandates Statement

    H.R. 4355 contains no unfunded mandates.

         XII. Committee Oversight Findings and Recommendations

    The Committee's oversight findings and recommendations are 
reflected in the body of this report.

      XIII. Statement on General Performance Goals and Objectives

    The goal of this legislation is to support research and 
development on technical and other tools to assist the public 
and private sectors in identifying manipulated and synthesized 
content online.

               XIV. Federal Advisory Committee Statement

    H.R. 4355 does not create any advisory committees.

                  XV. Duplication of Federal Programs

    Pursuant to clause 3(c)(5) of rule XIII of the Rules of the 
House of Representatives, the Committee finds that no provision 
of H.R. 4355 establishes or reauthorizes a program of the 
federal government known to be duplicative of another federal 
program, including any program that was included in a report to 
Congress pursuant to section 21 of Public Law 111-139 or the 
most recent Catalog of Federal Domestic Assistance.

                      XVI. Earmark Identification

    Pursuant to clause 9(e), 9(f), and 9(g) of rule XXI, the 
Committee finds that H.R. 4355 contains no earmarks, limited 
tax benefits, or limited tariff benefits.

             XVII. Applicability to the Legislative Branch

    The Committee finds that H.R. 4355 does not relate to the 
terms and conditions of employment or access to public services 
or accommodations within the meaning of section 102(b)(3) of 
the Congressional Accountability Act (Public Law 104-1).

     XVIII. Statement on Preemption of State, Local, or Tribal Law

    This bill is not intended to preempt any state, local, or 
tribal law.

       XIX. Changes in Existing Law Made by the Bill, as Reported

    This legislation does not amend any existing Federal 
statute.

              XX. Proceedings of the Full Committee Markup
              

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