AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models

• URL: http://arxiv.org/abs/2509.00641v1
• Date: Sun, 31 Aug 2025 00:00:03 GMT
• Title: AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models
• Authors: Zhipeng Yin, Zichong Wang, Avash Palikhe, Zhen Liu, Jun Liu, Wenbin Zhang,
• Abstract summary: This paper introduces Assessing and Mitigating Copyright Risks (AMCR)<n>AMCR builds upon prompt-based strategies by systematically restructuring risky prompts into safe and non-sensitive forms.<n>Experiments validate the effectiveness of AMCR in revealing and mitigating latent copyright risks.
• Score: 14.928831547948326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models have achieved impressive results in text to image tasks, significantly advancing visual content creation. However, this progress comes at a cost, as such models rely heavily on large-scale training data and may unintentionally replicate copyrighted elements, creating serious legal and ethical challenges for real-world deployment. To address these concerns, researchers have proposed various strategies to mitigate copyright risks, most of which are prompt based methods that filter or rewrite user inputs to prevent explicit infringement. While effective in handling obvious cases, these approaches often fall short in more subtle situations, where seemingly benign prompts can still lead to infringing outputs. To address these limitations, this paper introduces Assessing and Mitigating Copyright Risks (AMCR), a comprehensive framework which i) builds upon prompt-based strategies by systematically restructuring risky prompts into safe and non-sensitive forms, ii) detects partial infringements through attention-based similarity analysis, and iii) adaptively mitigates risks during generation to reduce copyright violations without compromising image quality. Extensive experiments validate the effectiveness of AMCR in revealing and mitigating latent copyright risks, offering practical insights and benchmarks for the safer deployment of generative models.

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