networks or other technologies that synthesize or manipulate 
content. 
(b) OUTREACH.—The Director of the National Insti-
tute 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 net-
works; and 
(2) to consider the feasibility of an ongoing pub-
lic and private sector engagement to develop vol-
untary standards for the function and outputs of gen-
erative adversarial networks or other technologies that 
synthesize or manipulate content. 
SEC. 5. REPORT ON FEASIBILITY OF PUBLIC-PRIVATE PART-
NERSHIP TO DETECT MANIPULATED OR SYN-
THESIZED 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 Transpor-
tation a report containing— 
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(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 ma-
nipulate 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 adver-
sarial 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 artifi-
cial 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 dis-
criminator create a feedback loop, causing the generator to 
produce increasingly higher-quality artificial outputs and 
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the discriminator to increasingly improve in detecting such 
artificial outputs. 
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Union Calendar No. 213 
116TH CONGRESS 
1ST SESSION 
H. R. 4355 
[Report No. 116–268] 
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 devel-
oped in the future, and for other purposes. 
NOVEMBER 5, 2019 
Reported with an amendment, committed to the Com-
mittee of the Whole House on the State of the Union, 
and ordered to be printed 
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