Generative ML and CSAM: Implications and Mitigations

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Abstract/Contents

Abstract: A joint report between the Stanford Internet Observatory and Thorn examines implications of fully realistic child sexual abuse material (CSAM) produced by generative machine learning models. Advances in the open-source generative ML community have led to increasingly realistic adult content, to the point that content indistinguishable from actual photographs is likely to be common in the very near future. These same models and techniques have been also leveraged to produce CSAM. We examine what has enabled this state of affairs, the potential societal consequences of the proliferation of such content, and measures that can be taken to minimize harm from current and future visual generative ML models.

Subject	Generative Machine Learning
Subject	AI Ethics
Subject	CSAM
Genre	Text
Genre	Report

DOI	https://doi.org/10.25740/jv206yg3793
Location	https://purl.stanford.edu/jv206yg3793

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License: This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International license (CC BY-NC-ND).

Preferred citation: Thiel, D., Stroebel, M., and Portnoff, R. (2023). Generative ML and CSAM: Implications and Mitigations. Stanford Digital Repository. Available at https://purl.stanford.edu/jv206yg3793. https://doi.org/10.25740/jv206yg3793.

Stanford Internet Observatory, Freeman Spogli Institute for International Studies

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