[Congressional Bills 116th Congress]
[From the U.S. Government Publishing Office]
[H.R. 4355 Referred in Senate (RFS)]
<DOC>
116th CONGRESS
1st Session
H. R. 4355
_______________________________________________________________________
IN THE SENATE OF THE UNITED STATES
December 10, 2019
Received; read twice and referred to the Committee on Commerce,
Science, and Transportation
_______________________________________________________________________
AN ACT
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 1 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.
Passed the House of Representatives December 9, 2019.
Attest:
CHERYL L. JOHNSON,
Clerk.