116TH CONGRESS 

1ST SESSION H. R. 4355 

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. 
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•HR 4355 EH

1 Be it enacted by the Senate and House of Representa2 tives of the United States of America in Congress assembled, 

3 SECTION 1. SHORT TITLE. 

4 This Act may be cited as the ‘‘Identifying Outputs 

5 of Generative Adversarial Networks Act’’ or the ‘‘IOGAN 

6 Act’’. 

7 SEC. 2. FINDINGS. 

8 Congress finds the following: 

9 (1) Research gaps currently exist on the under10 lying technology needed to develop tools to identify 

11 authentic videos, voice reproduction, or photos from 

12 manipulated or synthesized content, including those 

13 generated by generative adversarial networks. 

14 (2) The National Science Foundation’s focus to 

15 support research in artificial intelligence through 

16 computer and information science and engineering, 

17 cognitive science and psychology, economics and 

18 game theory, control theory, linguistics, mathe19 matics, and philosophy, is building a better under20 standing of how new technologies are shaping the 

21 society and economy of the United States. 

22 (3) The National Science Foundation has iden23 tified the ‘‘10 Big Ideas for NSF Future Invest24 ment’’ including ‘‘Harnessing the Data Revolution’’ 

25 and the ‘‘Future of Work at the Human-Technology 
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1 Frontier’’, in with artificial intelligence is a critical 

2 component. 

3 (4) The outputs generated by generative adver4 sarial networks should be included under the um5 brella of research described in paragraph (3) given 

6 the grave national security and societal impact po7 tential of such networks. 

8 (5) Generative adversarial networks are not 

9 likely to be utilized as the sole technique of artificial 

10 intelligence or machine learning capable of creating 

11 credible deepfakes and other comparable techniques 

12 may be developed in the future to produce similar 

13 outputs. 

14 SEC. 3. NSF SUPPORT OF RESEARCH ON MANIPULATED OR 

15 SYNTHESIZED CONTENT AND INFORMATION 

16 SECURITY. 

17 The Director of the National Science Foundation, in 

18 consultation with other relevant Federal agencies, shall 

19 support merit-reviewed and competitively awarded re20 search on manipulated or synthesized content and infor21 mation authenticity, which may include— 

22 (1) fundamental research on digital forensic 

23 tools or other technologies for verifying the authen24 ticity of information and detection of manipulated or 
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1 synthesized content, including content generated by 

2 generative adversarial networks; 

3 (2) fundamental research on technical tools for 

4 identifying manipulated or synthesized content, such 

5 as watermarking systems for generated media; 

6 (3) social and behavioral research related to 

7 manipulated or synthesized content, including the 

8 ethics of the technology and human engagement 

9 with the content; 

10 (4) research on public understanding and 

11 awareness of manipulated and synthesized content, 

12 including research on best practices for educating 

13 the public to discern authenticity of digital content; 

14 and 

15 (5) research awards coordinated with other fed16 eral agencies and programs including the Net17 working and Information Technology Research and 

18 Development Program, the Defense Advanced Re19 search Projects Agency and the Intelligence Ad20 vanced Research Projects Agency. 

21 SEC. 4. NIST SUPPORT FOR RESEARCH AND STANDARDS ON 

22 GENERATIVE ADVERSARIAL NETWORKS. 

23 (a) IN GENERAL.—The Director of the National In24 stitute of Standards and Technology shall support re25 search for the development of measurements and stand-
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1 ards necessary to accelerate the development of the tech2 nological tools to examine the function and outputs of gen3 erative adversarial networks or other technologies that 

4 synthesize or manipulate content. 

5 (b) OUTREACH.—The Director of the National Insti6 tute of Standards and Technology shall conduct out7 reach— 

8 (1) to receive input from private, public, and 

9 academic stakeholders on fundamental measure10 ments and standards research necessary to examine 

11 the function and outputs of generative adversarial 

12 networks; and 

13 (2) to consider the feasibility of an ongoing 

14 public and private sector engagement to develop vol15 untary standards for the function and outputs of 

16 generative adversarial networks or other technologies 

17 that synthesize or manipulate content. 

18 SEC. 5. REPORT ON FEASIBILITY OF PUBLIC-PRIVATE 

19 PARTNERSHIP TO DETECT MANIPULATED OR 

20 SYNTHESIZED CONTENT. 

21 Not later than 1 year after the date of the enactment 

22 of this Act, the Director of the National Science Founda23 tion and the Director of the National Institute of Stand24 ards and Technology shall jointly submit to the Committee 

25 on Space, Science, and Technology of the House of Rep-
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1 resentatives and the Committee on Commerce, Science, 

2 and Transportation a report containing— 

3 (1) the Directors’ findings with respect to the 

4 feasibility for research opportunities with the private 

5 sector, including digital media companies to detect 

6 the function and outputs of generative adversarial 

7 networks or other technologies that synthesize or 

8 manipulate content; and 

9 (2) any policy recommendations of the Direc10 tors that could facilitate and improve communication 

11 and coordination between the private sector, the Na12 tional Science Foundation, and relevant Federal 

13 agencies through the implementation of innovative 

14 approaches to detect digital content produced by 

15 generative adversarial networks or other technologies 

16 that synthesize or manipulate content. 

17 SEC. 6. GENERATIVE ADVERSARIAL NETWORK DEFINED. 

18 In this Act, the term ‘‘generative adversarial net19 work’’ means, with respect to artificial intelligence, the 

20 machine learning process of attempting to cause a gener21 ator artificial neural network (referred to in this para22 graph as the ‘‘generator’’ and a discriminator artificial 

23 neural network (referred to in this paragraph as a ‘‘dis24 criminator’’) to compete against each other to become 

25 more accurate in their function and outputs, through 
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1 which the generator and discriminator create a feedback 

2 loop, causing the generator to produce increasingly higher3 quality artificial outputs and the discriminator to increas4 ingly improve in detecting such artificial outputs. 

Passed the House of Representatives December 9, 

2019. 

Attest: 

Clerk. 
116TH CONGRESS 

1ST SESSION H. R. 4355 

AN ACT 

To direct the Director of the National Science 

oped in the future, and for other purposes. other comparable techniques that may be develnetworks, otherwise known as deepfakes, and that may be generated by generative adversarial Foundation to support research on the outputs