PUBLIC LAW 116–258—DEC. 23, 2020 IDENTIFYING OUTPUTS OF GENERATIVE ADVERSARIAL NETWORKS ACT VerDate Sep 11 2014 09:50 Jan 06, 2021 Jkt 019139 PO 00258 Frm 00001 Fmt 6579 Sfmt 6579 E:\PUBLAW\PUBL258.116 PUBL258 dkrause on LAP5T8D0R2PROD with PUBLAWS 134 STAT. 1150 PUBLIC LAW 116–258—DEC. 23, 2020 Public Law 116–258 116th Congress 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, other- wise 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) Gaps currently exist on the underlying research needed to develop tools that detect videos, audio files, or photos that have manipulated or synthesized content, including those gen- erated by generative adversarial networks. Research on digital forensics is also needed to identify, preserve, recover, and ana- lyze the provenance of digital artifacts. (2) The National Science Foundation’s focus to support research in artificial intelligence through computer and information science and engineering, cognitive science and psy- chology, economics and game theory, control theory, linguistics, mathematics, and philosophy, is building a better under- standing 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 ‘‘Har- nessing the Data Revolution’’ and the ‘‘Future of Work at the Human-Technology Frontier’’, with artificial intelligence is a critical component. (4) The outputs generated by generative adversarial net- works 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. Other techniques may be developed in the future to produce similar outputs. Identifying Outputs of Generative Adversarial Networks Act. 15 USC 9101 note. 15 USC 9101. Dec. 23, 2020 [S. 2904] VerDate Sep 11 2014 09:50 Jan 06, 2021 Jkt 019139 PO 00258 Frm 00002 Fmt 6580 Sfmt 6581 E:\PUBLAW\PUBL258.116 PUBL258 dkrause on LAP5T8D0R2PROD with PUBLAWS 134 STAT. 1151 PUBLIC LAW 116–258—DEC. 23, 2020 SEC. 3. NSF SUPPORT OF RESEARCH ON MANIPULATED OR SYN- THESIZED CONTENT AND INFORMATION SECURITY. The Director of the National Science Foundation, in consulta- tion 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 con- tent generated by generative adversarial networks; (2) fundamental research on technical tools for identifying manipulated or synthesized content, such as watermarking sys- tems for generated media; (3) social and behavioral research related to manipulated or synthesized content, including 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 Defense Advanced Research Projects Agency and the Intelligence Advanced Research Projects Agency, with coordination enabled by the Networking and Information Technology Research and Development Pro- gram. SEC. 4. NIST SUPPORT FOR RESEARCH AND STANDARDS ON GENERA- TIVE ADVERSARIAL NETWORKS. (a) IN GENERAL.—The Director of the National Institute of Standards and Technology shall support research for the develop- ment 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 Stand- ards 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 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 Science, Space, and Tech- nology of the House of Representatives, the Subcommittee on Com- merce, Justice, Science, and Related Agencies of the Committee on Appropriations of the House of Representatives, the Committee on Commerce, Science, and Transportation of the Senate, and the 15 USC 9103. Coordination. Consultation. 15 USC 9102. VerDate Sep 11 2014 09:50 Jan 06, 2021 Jkt 019139 PO 00258 Frm 00003 Fmt 6580 Sfmt 6581 E:\PUBLAW\PUBL258.116 PUBL258 dkrause on LAP5T8D0R2PROD with PUBLAWS 134 STAT. 1152 PUBLIC LAW 116–258—DEC. 23, 2020 LEGISLATIVE HISTORY—S. 2904 (H.R. 4355): HOUSE REPORTS: No. 116–268 (Comm. on Science, Space, and Technology) accom- panying H.R. 4355. SENATE REPORTS: No. 116–289 (Comm. on Commerce, Science, and Transporta- tion). CONGRESSIONAL RECORD, Vol. 166 (2020): Nov. 18, considered and passed Senate. Dec. 8, considered and passed House. Æ Subcommittee on Commerce, Justice, Science, and Related Agencies of the Committee on Appropriations of the Senate a report con- taining— (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 rel- evant Federal agencies through the implementation of innova- tive 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. Approved December 23, 2020. 15 USC 9104. Recommenda- tions. VerDate Sep 11 2014 09:50 Jan 06, 2021 Jkt 019139 PO 00258 Frm 00004 Fmt 6580 Sfmt 6580 E:\PUBLAW\PUBL258.116 PUBL258 dkrause on LAP5T8D0R2PROD with PUBLAWS