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Combatting deepfakes: Policies to address national security threats and rights violations

1 Pith paper cite this work. Polarity classification is still indexing.

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abstract

This paper provides policy recommendations to address threats from deepfakes. First, we provide background information about deepfakes and review the harms they pose. We describe how deepfakes are currently used to proliferate sexual abuse material, commit fraud, manipulate voter behavior, and pose threats to national security. Second, we review previous legislative proposals designed to address deepfakes. Third, we present a comprehensive policy proposal that focuses on addressing multiple parts of the deepfake supply chain. The deepfake supply chain begins with a small number of model developers, model providers, and compute providers, and it expands to include billions of potential deepfake creators. We describe this supply chain in greater detail and describe how entities at each step of the supply chain ought to take reasonable measures to prevent the creation and proliferation of deepfakes. Finally, we address potential counterpoints of our proposal. Overall, deepfakes will present increasingly severe threats to global security and individual liberties. To address these threats, we call on policymakers to enact legislation that addresses multiple parts of the deepfake supply chain.

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cs.SD 1

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2025 1

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CONDITIONAL 1

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representative citing papers

AI-Generated Music Detection and its Challenges

cs.SD · 2025-01-17 · conditional · novelty 6.0

A simple convolutional classifier detects autoencoder-generated music with 99.8% accuracy, but accuracy collapses under audio manipulation and on unseen generator families.

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Showing 1 of 1 citing paper.

  • AI-Generated Music Detection and its Challenges cs.SD · 2025-01-17 · conditional · none · ref 34 · internal anchor

    A simple convolutional classifier detects autoencoder-generated music with 99.8% accuracy, but accuracy collapses under audio manipulation and on unseen generator families.