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arXiv · 2512.15442

Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting

Neeraj Sarna·Yuanyuan Li·Michael von Gablenz
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Abstract

Large scale text-to-image generation models can memorize and reproduce their training dataset. Since the training dataset often contains copyrighted material, reproduction of training dataset poses a copyright infringement risk, which could result in legal liabilities and financial losses for both the AI user and the developer. The current works explores the potential of chain-of-thought and task instruction prompting in reducing copyrighted content generation. To this end, we present a formulation that combines these two techniques with two other copyright mitigation strategies: a) negative prompting, and b) prompt re-writing. We study the generated images in terms their similarity to a copyrighted image and their relevance of the user input. We present numerical experiments on a variety of models and provide insights on the effectiveness of the aforementioned techniques for varying model complexity.

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Neeraj Sarna, Yuanyuan Li, Michael von Gablenz. 2025-12-17. Copyright Infringement Risk Reduction via Chain-of-Thought and Task Instruction Prompting. https://arxiv.org/abs/2512.15442

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