I 

116TH CONGRESS 

1ST SESSION 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. 

IN THE HOUSE OF REPRESENTATIVES 

SEPTEMBER 17, 2019 

Mr. GONZALEZ of Ohio (for himself, Ms. STEVENS, Mr. BAIRD, and Ms. HILL

of California) introduced the following bill; which was referred to the 

Committee on Science, Space, and Technology 

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 developed in the future, and for other purposes. 

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’’. 


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1 SEC. 2. FINDINGS. 

2 Congress finds the following: 

3 (1) Research gaps currently exist on the under4 lying technology needed to develop tools to identify 

5 authentic videos, voice reproduction, or photos from 

6 those generated by generative adversarial networks. 

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

8 support research in artificial intelligence through 

9 computer and information science and engineering, 

10 cognitive science and psychology, economics and 

11 game theory, control theory, linguistics, mathe12 matics, and philosophy, is building a better under13 standing of how new technologies are shaping the 

14 society and economy of the United States. 

15 (3) The National Science Foundation has iden16 tified the ‘‘10 Big Ideas for NSF Future Invest17 ment’’ including ‘‘Harnessing the Data Revolution’’ 

18 and the ‘‘Future of Work at the Human-Technology 

19 Frontier’’, in with artificial intelligence is a critical 

20 component. 

21 (4) The outputs generated by generative adver22 sarial networks should be included under the um23 brella of research described in paragraph (3) given 

24 the grave national security and societal impact po25 tential of such networks. 


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1 (5) Generative adversarial networks are not 

2 likely to be utilized as the sole technique of artificial 

3 intelligence or machine learning capable of creating 

4 credible deepfakes and other comparable techniques 

5 may be developed in the future to produce similar 

6 outputs. 

7 SEC. 3. NSF SUPPORT OF RESEARCH FOR OUTPUTS OF 

8 GENERATIVE ADVERSARIAL NETWORKS. 

9 The Director of the National Science Foundation, in 

10 consultation with other relevant Federal agencies, shall 

11 support merit-reviewed and competitively awarded re12 search on the science and ethics of material produced by 

13 generative adversarial networks, which may include— 

14 (1) supplementing fundamental research on dig15 ital media forensic tools or comparable technologies 

16 for detection of the outputs of generative adversarial 

17 networks completed by the Defense Advanced Re18 search Projects Agency and the Intelligence Ad19 vanced Research Projects Activity; 

20 (2) fundamental research on developing con21 straint aware generative adversarial networks; and 

22 (3) social and behavioral research on the ethics 

23 of the technology, and human engagement with the 

24 networks. 


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

2 GENERATIVE ADVERSARIAL NETWORKS. 

3 (a) IN GENERAL.—The Director of the National In4 stitute of Standards and Technology shall support re5 search for the development of measurements and stand6 ards necessary to accelerate the development of the tech7 nological tools to examine the function and outputs of gen8 erative adversarial networks. 

9 (b) OUTREACH.—The Director of the National Insti10 tute of Standards and Technology shall conduct out11 reach— 

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

13 academic stakeholders on fundamental measure14 ments and standards research necessary to examine 

15 the function and outputs of generative of generative 

16 adversarial networks or to develop constraint aware 

17 generative adversarial networks; and 

18 (2) to consider the feasibility of an ongoing 

19 public and private sector engagement to develop vol20 untary standards for the outputs of generative ad21 versarial networks or comparable technologies. 


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

2 PARTNERSHIP TO DETECT OUTPUTS OF GEN3 ERATIVE ADVERSARIAL NETWORKS AND 

4 COMPARABLE TECHNOLOGIES. 

5 Not later than one year after the date of the enact6 ment of this Act, the Director of the National Science 

7 Foundation and the Director of the National Institute of 

8 Standards and Technology shall jointly submit to the 

9 Committee on Science, Space, and Technology of the 

10 House of Representatives and the Committee on Com11 merce, Science, and Transportation a report containing— 

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

13 feasibility for research opportunities with the private 

14 sector, including digital media companies to detect 

15 the outputs of generative adversarial networks or 

16 comparable technologies; and 

17 (2) any policy recommendations of the Direc18 tors that could facilitate and improve communication 

19 and coordination between the private sector, the Na20 tional Science Foundation, and relevant Federal 

21 agencies through the implementation of innovative 

22 approaches to detect media products produced by 

23 generative adversarial networks or comparable tech24 nologies. 

25 SEC. 6. DEFINITIONS. 

26 In this Act: 


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1 (1) GENERATIVE ADVERSARIAL NETWORK.— 

2 The term ‘‘generative adversarial network’’ means, 

3 with respect to artificial intelligence, the machine 

4 learning process of attempting to cause a generator 

5 artificial neural network (referred to in this para6 graph as the ‘‘generator’’ and a discriminator artifi7 cial neural network (referred to in this paragraph as 

8 a ‘‘discriminator’’)) to compete against each other to 

9 become more accurate in their function and outputs, 

10 through which the generator and discriminator cre11 ate a feedback loop, causing the generator to 

12 produce increasingly higher-quality artificial outputs 

13 and the discriminator to increasingly improve in de14 tecting such artificial outputs. 

15 (2) COMPARABLE TECHNOLOGY.—The term 

16 ‘‘comparable technology’’ means technology that uti17 lizes similar techniques to achieve the same outputs 

18 as a generative adversarial network. 

19 (3) CONSTRAINT AWARE.—The term ‘‘con20 straint aware’’ means, with respect to artificial intel21 ligence, the generation of realistic relational data by 

22 a machine with constraint on the modules generated 

23 by an adversarial network. 

Æ 


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