LLM 학습 데이터 저작권 검증을 위한 오픈소스 플랫폼

📝 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.

💡 Analysis

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.

📄 Content

Copyright Detection in Large Language Models: An Ethical Approach to Generative AI Development David Szczecina University of Waterloo david.szczecina@uwaterloo.ca Senan Gaffori University of Waterloo senan.gaffori@uwaterloo.ca Edmond Li University of Waterloo e26li@uwaterloo.com Abstract—The widespread use of Large Language Models (LLMs) raises critical concerns regarding the unauthorized inclu- sion 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. I. INTRODUCTION A. Motivation Large Language Models (LLMs) such as GPT-4 and Claude have revolutionized natural language processing, but also raise legal and ethical concerns about the unauthorized use of copy- righted content in training datasets [1]. Proprietary models often rely on large-scale web scraping [2], incorporating copyrighted material without clear consent mechanisms, compensation, and intellectual property protection [3]. A major concern is the lack of compensation for content creators whose work is used without permission. Legal frame- works for AI copyright enforcement are rapidly evolving, with landmark cases like New York Times v. OpenAI [4] bringing increased scrutiny to dataset curation. Transparency in AI training datasets is essential to ensure responsible and ethical development. Research indicates that as models increase in size, memorization tendencies become more pronounced, particularly in models exceeding 100 billion parameters [4], increasing the risk of unauthorized reproduction of copyrighted content. Current detection methods, such as plagiarism checkers and statistical techniques, struggle to identify subtly paraphrased copyrighted content [2] [5]. While frameworks such as DE- COP offer promising approaches, they remain computationally expensive and complex; making them impractical for inde- pendent creators and smaller organizations. A scalable, cost- effective, and user-friendly solution is needed to verify whether copyrighted works have been used in LLM training datasets. Fig. 1. Unique passages are extracted and paraphrased from a users content, next an LLM is prompted to determine the original passage. Final scores show the probability of the copyrighted content being used in training the LLM B. Related Works The detection of copyrighted content in LLM training datasets has been the subject of increasing research attention, particularly as legal and ethical concerns surrounding dataset curation intensify. While traditional plagiarism detection tools struggle to identify AI-generated reproductions of proprietary content [2], several machine learning-based approaches have been proposed to address this issue. Membership inference attacks [6] analyze a model’s con- fidence scores to determine whether a given text sample was likely included in the training data. Although effective in controlled experiments, this approach requires adversarial access to the model and often produces inconclusive results due to dataset augmentation and model fine-tuning techniques. Sim- ilarly, perplexity-based analysis is another detection approach by evaluating how confidently an LLM predicts a passage of text [7]. Low perplexity scores suggest memorization, however, this method struggles to distinguish between legally sourced and unauthorized content, making it unreliable for copyright enforcement. Another proposed approach is digital watermarking [8], where imperceptible markers are embedded into text data before model training. While useful for tracking known copyrighted works, watermarking is ineffective against existing datasets that were scraped from the web and fails to detect content that has been paraphrased or restructured. A more recent approach, DE-COP: Detecting Copyrighted Content in Language Models Training Data, [2], introduces a method to determine whether a language model has memorized copyrighted content. Unlike statistical approaches, DE-COP introduces a multiple-choice question-answering framework, arXiv:2511.20623v1 [cs.AI] 25 Nov 2025 where an LLM must distinguish an original verbatim passage from paraphrased alternatives. If a model consistently selects the correct passage, this sugg

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