S. 2904 — what changed
Identifying Outputs of Generative Adversarial Networks Act
From Reported in Senate to Engrossed in Senate. 3 sections amended between Reported in Senate and Engrossed in Senate.
Sec. 2 Findings
Congress finds the following:
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Research gaps Gaps currently exist on the underlying technology research needed to develop tools to identify authentic that detect videos, voice reproduction, audio files, or photos from that have manipulated or synthesized content, including those generated by generative adversarial networks.networks. Research on digital forensics is also needed to identify, preserve, recover, and analyze the provenance of digital artifacts.
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.
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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.
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.
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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 comparable techniques may be developed in the future to produce similar outputs.
Sec. 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—
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;
fundamental research on technical tools for identifying manipulated or synthesized content, such as watermarking systems for generated media;
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social and behavioral research related to manipulated or synthesized content, including the ethics of the technology and human engagement with the content;
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
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research awards coordinated with other Federal federal agencies and programs, including the Networking and Information Technology Research and Development Program, the Defense Advanced Research Projects Agency, Agency and the Intelligence Advanced Research Projects Agency.Agency, with coordination enabled by the Networking and Information Technology Research and Development Program.
Sec. 6 Generative adversarial network defined
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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”) “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.