Copyright Detection in Large Language Models: An Ethical Approach to Generative AI Development

2025-11-25

AI Summary

Unauthorized use of copyrighted material in large language model (LLM) training data poses escalating legal and ethical challenges. Existing detection methods—such as DE-COP—are computationally intensive and operationally complex, rendering them inaccessible to independent creators. This paper introduces a lightweight, scalable, and open-source copyright detection framework. Building upon DE-COP, it integrates an efficient API invocation strategy, an optimized locality-sensitive hashing (LSH) algorithm for similarity matching, and a modular backend architecture, complemented by an intuitive web frontend. Relative to baseline approaches, the framework achieves 10–30% higher detection efficiency while substantially reducing computational overhead and lowering deployment barriers. The fully open-sourced platform enables creators to autonomously verify whether their works have been incorporated into LLM training corpora, thereby advancing compliance, transparency, and responsible copyright governance in AI development.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The widespread use of Large Language Models (LLMs) raises critical concerns regarding the unauthorized inclusion of copyrighted content in training data. Existing detection frameworks, such as DE-COP, are computationally intensive, and largely inaccessible to independent creators. As legal scrutiny increases, there is a pressing need for a scalable, transparent, and user-friendly solution. This paper introduce an open-source copyright detection platform that enables content creators to verify whether their work was used in LLM training datasets. Our approach enhances existing methodologies by facilitating ease of use, improving similarity detection, optimizing dataset validation, and reducing computational overhead by 10-30% with efficient API calls. With an intuitive user interface and scalable backend, this framework contributes to increasing transparency in AI development and ethical compliance, facilitating the foundation for further research in responsible AI development and copyright enforcement.

Problem

• Detecting unauthorized copyrighted content in LLM training data
• Providing accessible copyright verification tools for independent creators
• Reducing computational overhead in existing copyright detection frameworks

Innovation

• Open-source platform for copyright detection in LLMs
• Enhanced similarity detection with reduced computational overhead
• User-friendly interface with scalable backend architecture