Identifying and Eliminating CSAM in Generative ML Training Data and Models

Abstract

Generative Machine Learning models have been well documented as being able to produce explicit adult content, including child sexual abuse material (CSAM) as well as to alter benign imagery of a clothed victim to produce nude or explicit content. In this study, we examine the LAION-5B dataset—parts of which were used to train the popular Stable Diffusion series of models—to attempt to measure to what degree CSAM itself may have played a role in the training process of models trained on this dataset. We use a combination of PhotoDNA perceptual hash matching, cryptographic hash matching, k-nearest neighbors queries and ML classifiers.

This methodology detected many hundreds of instances of known CSAM in the training set, as well as many new candidates that were subsequently verified by outside parties. We also provide recommendations for mitigating this issue for those that need to maintain copies of this training set, building future training sets, altering existing models and the hosting of models trained on LAION-5B.

Use and reproduction

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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. (2023). Identifying and Eliminating CSAM in Generative ML Training Data and Models. Stanford Digital Repository. Available at https://purl.stanford.edu/kh752sm9123. https://doi.org/10.25740/kh752sm9123.

Each version has a distinct URL, but you can use this PURL to access the latest version.
https://purl.stanford.edu/kh752sm9123

Contact

dthiel@stanford.edu

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