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Cited by 33Digital Media Forensic DetectionHate Speech and Cyberbullying DetectionAdversarial Robustness in Machine Learning

The DOI matches its DataCite record.

Identifying and Eliminating CSAM in Generative ML Training Data and Models

Open access
Thiel, David, Hancock, Jeffrey·2025·Stanford Digital Repository
DOI 10.25740/kh752sm9123
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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.

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Metadata

Authors
Thiel, D., & Hancock, J.
ORCID
Venue
Stanford Digital Repository
Publisher
Stanford Digital Repository
Published
2025
DOI
10.25740/kh752sm9123
OpenAlex
W6946089209
Cited by
33
References
0
Citations by year
2026: 12 · 2025: 14
FWCI
2.46
Type
journal article
Language
en
Open access
Licence
creativecommons.org/licenses/by-nc-nd/4.0/legalcode
Locations
Stanford Digital Repository
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords
child pornography, generative AI training data, Stable Diffusion, data poisoning, training data integrity, perceptual hashing, image classification

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