We present Copyright Detective, the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. The system treats copyright infringement versus compliance as an evidence discovery process rather than a static classification task due to the complex nature of copyright law. It integrates multiple detection paradigms, including content recall testing, paraphrase-level similarity analysis, persuasive jailbreak probing, and unlearning verification, within a unified and extensible framework. Through interactive prompting, response collection, and iterative workflows, our system enables systematic auditing of verbatim memorization and paraphrase-level leakage, supporting responsible deployment and transparent evaluation of LLM copyright risks even with black-box access.

翻译：我们提出了版权侦探，这是首个用于检测、分析和可视化大语言模型输出中潜在版权风险的交互式取证系统。由于版权法的复杂性，该系统将版权侵权与合规视为一个证据发现过程，而非静态的分类任务。它在一个统一且可扩展的框架内集成了多种检测范式，包括内容召回测试、释义级相似性分析、说服性越狱探测和反学习验证。通过交互式提示、响应收集和迭代工作流，我们的系统能够对逐字记忆和释义级泄露进行系统性审计，即使在黑盒访问条件下，也支持负责任地部署和透明地评估大语言模型的版权风险。