Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks
- URL: http://arxiv.org/abs/2602.05252v2
- Date: Wed, 11 Feb 2026 03:48:26 GMT
- Title: Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks
- Authors: Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi Ma, Yun Huang, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang,
- Abstract summary: Copyright Detective is an interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs.<n>It integrates multiple detection paradigms, including content recall testing, paraphrase-level similarity analysis, persuasive probing, and unlearning verification.
- Score: 123.36265437655187
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment [63.662126457336534]
OpenNovelty is an agentic system for transparent, evidence-based novelty analysis.<n>It grounds all assessments in retrieved real papers, ensuring verifiable judgments.<n>OpenNovelty aims to empower the research community with a scalable tool that promotes fair, consistent, and evidence-backed peer review.
arXiv Detail & Related papers (2026-01-04T15:48:51Z) - Who's Your Judge? On the Detectability of LLM-Generated Judgments [37.318998323301365]
Large Language Model (LLM)-based judgments leverage powerful LLMs to efficiently evaluate candidate content and provide judgment scores.<n>In this work, we propose and formalize the task of judgment detection.<n>We introduce textitJ-Detector, a lightweight and transparent neural detector augmented with explicitly extracted linguistic and LLM-enhanced features.
arXiv Detail & Related papers (2025-09-29T17:54:57Z) - CoTGuard: Using Chain-of-Thought Triggering for Copyright Protection in Multi-Agent LLM Systems [55.57181090183713]
We introduce CoTGuard, a novel framework for copyright protection that leverages trigger-based detection within Chain-of-Thought reasoning.<n>Specifically, we can activate specific CoT segments and monitor intermediate reasoning steps for unauthorized content reproduction by embedding specific trigger queries into agent prompts.<n>This approach enables fine-grained, interpretable detection of copyright violations in collaborative agent scenarios.
arXiv Detail & Related papers (2025-05-26T01:42:37Z) - CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models [58.58208005178676]
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.
arXiv Detail & Related papers (2025-02-21T08:09:07Z) - Measuring Copyright Risks of Large Language Model via Partial Information Probing [14.067687792633372]
We explore the data sources used to train Large Language Models (LLMs)
We input a portion of a copyrighted text into LLMs, prompt them to complete it, and then analyze the overlap between the generated content and the original copyrighted material.
Our findings demonstrate that LLMs can indeed generate content highly overlapping with copyrighted materials based on these partial inputs.
arXiv Detail & Related papers (2024-09-20T18:16:05Z) - Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data? [62.72729485995075]
We investigate the effectiveness of watermarking as a deterrent against the generation of copyrighted texts.<n>We find that watermarking adversely affects the success rate of Membership Inference Attacks (MIAs)<n>We propose an adaptive technique to improve the success rate of a recent MIA under watermarking.
arXiv Detail & Related papers (2024-07-24T16:53:09Z) - Evaluating Copyright Takedown Methods for Language Models [100.38129820325497]
Language models (LMs) derive their capabilities from extensive training on diverse data, including potentially copyrighted material.
This paper introduces the first evaluation of the feasibility and side effects of copyright takedowns for LMs.
We examine several strategies, including adding system prompts, decoding-time filtering interventions, and unlearning approaches.
arXiv Detail & Related papers (2024-06-26T18:09:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.