CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2502.15278v2
• Date: Sun, 27 Jul 2025 07:32:31 GMT
• Title: CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models
• Authors: Shunchang Liu, Zhuan Shi, Lingjuan Lyu, Yaochu Jin, Boi Faltings,
• Abstract summary: We propose CopyJudge, a novel automated infringement identification framework.<n>We employ an abstraction-filtration-comparison test framework to assess the likelihood of infringement.<n>We introduce a general LVLM-based mitigation strategy that automatically optimize infringing prompts.
• Score: 58.58208005178676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Assessing whether AI-generated images are substantially similar to source works is a crucial step in resolving copyright disputes. In this paper, we propose CopyJudge, a novel automated infringement identification framework that leverages large vision-language models (LVLMs) to simulate practical court processes for determining substantial similarity between copyrighted images and those generated by text-to-image diffusion models. Specifically, we employ an abstraction-filtration-comparison test framework based on the multi-LVLM debate to assess the likelihood of infringement and provide detailed judgment rationales. Based on these judgments, we further introduce a general LVLM-based mitigation strategy that automatically optimizes infringing prompts by avoiding sensitive expressions while preserving the non-infringing content. Furthermore, assuming the input noise is controllable, our approach can be enhanced by iteratively exploring non-infringing noise vectors within the diffusion latent space, even without modifying the original prompts. Experimental results show that our automated identification method achieves comparable state-of-the-art performance, while offering superior generalization and interpretability across various forms of infringement, and that our mitigation method more effectively mitigates memorization and IP infringement with a high degree of alignment to the original non-infringing expressions.

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