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

2025-12-17

AI Summary

To mitigate copyright infringement risks arising from training-data memorization in text-to-image (T2I) models, this paper proposes the first end-to-end framework integrating chain-of-thought (CoT) prompting, task-instruction reinforcement, negative prompting, and dynamic prompt rewriting. Methodologically, it pioneers the synergistic use of CoT reasoning and task instructions to suppress copyright leakage, and introduces a novel dual-dimensional evaluation paradigm—assessing both *copyright similarity* and *user-intent relevance*. Extensive experiments across mainstream T2I models—including SDXL and DALL·E 3—demonstrate that our approach reduces copyright content reproduction by up to 63%, while degrading CLIP Score by less than 8%, significantly outperforming baseline methods. The framework offers a scalable, interpretable, and compliance-oriented risk-mitigation pathway for responsible deployment of generative AI systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Reducing copyright infringement risk in text-to-image models
• Mitigating memorization and reproduction of copyrighted training data
• Evaluating prompting techniques for decreasing copyrighted content generation

Innovation

• Combines chain-of-thought with task instruction prompting
• Integrates negative prompting and prompt re-writing strategies
• Evaluates similarity to copyrighted images and input relevance