[Congressional Record Volume 165, Number 196 (Monday, December 9, 2019)]
[House]
[Pages H9363-H9364]
From the Congressional Record Online through the Government Publishing Office [www.gpo.gov]




       IDENTIFYING OUTPUTS OF GENERATIVE ADVERSARIAL NETWORKS ACT

  Ms. JOHNSON of Texas. Mr. Speaker, I move to suspend the rules and 
pass 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, as amended.
  The Clerk read the title of the bill.
  The text of the bill is as follows:

                               H.R. 4355

       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--

[[Page H9364]]

       (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.

  The SPEAKER pro tempore. Pursuant to the rule, the gentlewoman from 
Texas (Ms. Johnson) and the gentleman from Oklahoma (Mr. Lucas) each 
will control 20 minutes.
  The Chair recognizes the gentlewoman from Texas.


                             General Leave

  Ms. JOHNSON of Texas. Mr. Speaker, I ask unanimous consent that all 
Members may have 5 legislative days within which to revise and extend 
their remarks and include extraneous material on H.R. 4355, the bill 
under consideration.
  The SPEAKER pro tempore. Is there objection to the request of the 
gentlewoman from Texas?
  There was no objection.
  Ms. JOHNSON of Texas. Mr. Speaker, I yield myself such time as I may 
consume.
  Mr. Speaker, I rise today in support of H.R. 4355, the Identifying 
Outputs of Generative Adversarial Networks Act.
  Deepfake technology, which manipulates photos, videos, or audio clips 
to produce content that seems real but is not, has become increasingly 
commonplace in recent years. This increase in prevalence has been 
spurred, in part, by increases in computing power, widespread 
availability of images and other data, and the use of artificial 
intelligence.
  In many cases, the applications of this technology may be benign, but 
bad actors can also use this technology to spread disinformation and 
cause great harm to individuals, organizations, and society as a whole.
  During the Science, Space, and Technology Committee hearing on online 
imposters and disinformation earlier this year, one of the witnesses 
showed us a demonstration of a deepfake video in which he swapped the 
likenesses of two Members of Congress at the hearing.
  Despite the spread and potential harm of deepfake technology, there 
are currently no sure-fire methods of identifying and distinguishing 
manipulated content from authentic content. The ability to 
differentiate between manipulated and authentic content is essential to 
maintaining our national and economic security and protecting against 
malicious use of these technologies.
  H.R. 4355 leverages the strengths of the National Science Foundation 
and the National Institute of Standards and Technology by directing 
these agencies to support research on manipulated or synthesized 
content in order to help develop the standards and other tools 
necessary to detect this content.
  I commend my colleagues Representatives Gonzalez, Stevens, and Baird 
for their excellent leadership on this bipartisan legislation. I urge 
all of my colleagues to join in passing this bill.
  Mr. Speaker, I reserve the balance of my time.
  Mr. LUCAS. Mr. Speaker, I yield myself such time as I may consume.
  Mr. Speaker, I rise in support of H.R. 4355, the Identifying Outputs 
of Generative Adversarial Networks Act introduced by Representative 
Anthony Gonzalez. This bill addresses the underlying technologies for 
digital content commonly referred to as ``deepfakes.'' This technology 
uses machine learning to manipulate videos and other digital content to 
produce misleading and false products.
  These technologies are becoming more sophisticated and, in the wrong 
hands, present a serious security threat. As we know, bad actors are 
already using disinformation to disrupt civil society and try to sow 
divisions among Americans.
  H.R. 4355 supports the fundamental research necessary to better 
understand the underlying technology, to develop tools to identify 
manipulated content, and to better understand how humans interact with 
this generated content.
  The bill also tasks the National Institute of Standards and 
Technology with bringing together the private sector and government 
agencies to discuss how to advance innovation in this area responsibly.
  I applaud Mr. Gonzalez' bipartisan work on this bill and his 
leadership on the issue of technology and security.
  I thank the chairwoman and her staff for moving H.R. 4355 forward. 
There is a lot of fundamental research that needs to be done to better 
understand the technologies driving deepfakes and their impact on 
society. H.R. 4355 will help support that research.
  Mr. Speaker, I urge my colleagues to support the bill, and I yield 
back the balance of my time.
  Ms. JOHNSON of Texas. Mr. Speaker, I would like to express my 
appreciation for all the Members who have been working on this very 
important bipartisan legislation. I urge its passage, and I yield back 
the balance of my time.

                              {time}  1545

  The SPEAKER pro tempore. The question is on the motion offered by the 
gentlewoman from Texas (Ms. Johnson) that the House suspend the rules 
and pass the bill, H.R. 4355, as amended.
  The question was taken; and (two-thirds being in the affirmative) the 
rules were suspended and the bill, as amended, was passed.
  A motion to reconsider was laid on the table.

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