DIS-CO: Discovering Copyrighted Content in VLMs Training Data

• URL: http://arxiv.org/abs/2502.17358v2
• Date: Tue, 25 Feb 2025 10:10:35 GMT
• Title: DIS-CO: Discovering Copyrighted Content in VLMs Training Data
• Authors: André V. Duarte, Xuandong Zhao, Arlindo L. Oliveira, Lei Li,
• Abstract summary: We propose DIS-CO, a novel approach to infer the inclusion of copyrighted content during the model's development.<n>By repeatedly querying a VLM with specific frames from targeted copyrighted material, DIS-CO extracts the content's identity through free-form text completions.<n>Our results show that DIS-CO significantly improves detection performance, nearly doubling the average AUC of the best prior method.
• Score: 24.15936677068714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How can we verify whether copyrighted content was used to train a large vision-language model (VLM) without direct access to its training data? Motivated by the hypothesis that a VLM is able to recognize images from its training corpus, we propose DIS-CO, a novel approach to infer the inclusion of copyrighted content during the model's development. By repeatedly querying a VLM with specific frames from targeted copyrighted material, DIS-CO extracts the content's identity through free-form text completions. To assess its effectiveness, we introduce MovieTection, a benchmark comprising 14,000 frames paired with detailed captions, drawn from films released both before and after a model's training cutoff. Our results show that DIS-CO significantly improves detection performance, nearly doubling the average AUC of the best prior method on models with logits available. Our findings also highlight a broader concern: all tested models appear to have been exposed to some extent to copyrighted content. Our code and data are available at https://github.com/avduarte333/DIS-CO

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