IOGAN Act

Bill: 116-hr-4355
Sponsor: full_name: Rep. Gonzalez, Anthony [R-OH-16]
Introduced: 2019-09-17
Policy area: Science, Technology, Communications
Subjects: Advanced technology and technological innovations

Computers and information technology

Congressional oversight

Digital media

Government studies and investigations

Intergovernmental relations

Photography and imaging

Research administration and funding

Research and development

Research ethics

Technology assessment
Latest action: action_date: 2019-12-10
text: Received in the Senate and Read twice and referred to the Committee on Commerce, Science, and Transportation.

Summaries:
action_date: 2019-09-17
action_desc: Introduced in House
text: <p><b>Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act</b></p> <p>This bill directs the National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST) to support research on generative adversarial networks. A generative adversarial network is a software system designed to be trained with authentic inputs (e.g., photographs) to generate similar, but artificial, outputs (e.g., deepfakes).</p> <p>Specifically, the NSF must support research on the science and ethics of material produced by generative adversarial networks and NIST must support research to accelerate the development of tools to examine the function and outputs of generative adversarial networks.</p>

action_date: 2019-11-05
action_desc: Reported to House
text: <p><b>Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act</b></p> <p>This bill directs the National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST) to support research on manipulated or synthesized media, including the output of generative adversarial networks. A generative adversarial network is a software system designed to be trained with authentic inputs (e.g., photographs) to generate similar, but artificial, outputs (e.g., deepfakes).</p> <p>Specifically, the NSF must support research on manipulated or synthesized content and information authenticity and NIST must 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.</p>

action_date: 2019-12-09
action_desc: Passed House
text: <p><b>Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act</b></p> <p>(Sec. 3) This bill directs the National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST) to support research on manipulated or synthesized media, including the output of generative adversarial networks. A generative adversarial network is a software system designed to be trained with authentic inputs (e.g., photographs) to generate similar, but artificial, outputs (e.g., deepfakes).</p> <p>Specifically, the NSF must support research on manipulated or synthesized content and information authenticity.</p> <p> (Sec. 4) NIST must 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.</p> <p>NIST shall conduct outreach to (1) 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) consider the feasibility of an ongoing public and private sector engagement to develop voluntary standards for the function and outputs of such networks or other technologies that synthesize or manipulate content.</p> <p>(Sec. 5) The NSF and NIST must jointly submit to Congress a report containing (1) such agencies' 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 those agencies that could facilitate and improve communication and coordination between the private sector, the NSF, and relevant federal agencies through the implementation of innovative approaches to detect digital content produced by such networks or such technologies. </p>

tv_txt: I 116th CONGRESS 1st Session H. R. 4355 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. Short title This Act may be cited as the Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act . 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 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. 3. NSF support of research for outputs of generative adversarial networks The Director of the National Science Foundation, in consultation with other relevant Federal agencies, shall support merit-reviewed and competitively awarded research on the science and ethics of material produced by generative adversarial networks, which may include— (1) supplementing fundamental research on digital media forensic tools or comparable technologies for detection of the outputs of generative adversarial networks completed by the Defense Advanced Research Projects Agency and the Intelligence Advanced Research Projects Activity; (2) fundamental research on developing constraint aware generative adversarial networks; and (3) social and behavioral research on the ethics of the technology, and human engagement with the networks. 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. (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 of generative adversarial networks or to develop constraint aware generative adversarial networks; and (2) to consider the feasibility of an ongoing public and private sector engagement to develop voluntary standards for the outputs of generative adversarial networks or comparable technologies. 5. Report on feasibility of public-private partnership to detect outputs of generative adversarial networks and comparable technologies 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 Science, Space, 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 outputs of generative adversarial networks or comparable technologies; 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 media products produced by generative adversarial networks or comparable technologies. 6. Definitions In this Act: (1) Generative adversarial network 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. (2) Comparable technology The term comparable technology means technology that utilizes similar techniques to achieve the same outputs as a generative adversarial network. (3) Constraint aware The term constraint aware means, with respect to artificial intelligence, the generation of realistic relational data by a machine with constraint on the modules generated by an adversarial network.

tv_txt: IB Union Calendar No. 213 116th CONGRESS 1st Session H. R. 4355 [Report No. 116–268] 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 November 5, 2019 Additional sponsors: Ms. Sherrill , Ms. Wexton , Miss González-Colón of Puerto Rico , Ms. Slotkin , Mr. Lucas , and Ms. Spanberger Reported with an amendment, committed to the Committee of the Whole House on the State of the Union, and ordered to be printed Strike out all after the enacting clause and insert the part printed in italic For text of introduced bill, see copy of bill as introduced on September 17, 2019 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. Short title This Act may be cited as the Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act . 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. 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. 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. 5. Report on feasibility of public-private partnership to detect manipulated or synthesized 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 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. 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.

November 5, 2019 Reported with an amendment, committed to the Committee of the Whole House on the State of the Union, and ordered to be printed

tv_txt: IB 116th CONGRESS 1st Session H. R. 4355 IN THE HOUSE OF REPRESENTATIVES 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.

1. Short title This Act may be cited as the Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act . 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. 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. 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. 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. 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. Cheryl L. Johnson, Clerk.

tv_txt: IIB 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.

1. Short title This Act may be cited as the Identifying Outputs of Generative Adversarial Networks Act or the IOGAN Act . 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. 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. 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. 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. 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. Cheryl L. Johnson, Clerk