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[2506.19881] Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
Summary
This paper explores the concept of copyright protection for generative models, introducing a framework that defines conditions under which outputs can avoid copyright infringement.
Why It Matters
As generative AI technologies evolve, understanding copyright implications is crucial for developers and legal experts. This research addresses the need for clear guidelines to ensure that generative models operate within legal boundaries, promoting innovation while respecting intellectual property rights.
Key Takeaways
- The concept of near access-freeness (NAF) is insufficient for copyright protection.
- The paper introduces a blameless copyright protection framework to mitigate infringement risks.
- Clean-room copyright protection allows users to manage their copying risks effectively.
- Differential privacy can provide guarantees for copyright protection under certain conditions.
- The research emphasizes the importance of legal and technical foundations in copyright discussions.
Computer Science > Cryptography and Security arXiv:2506.19881 (cs) [Submitted on 23 Jun 2025 (v1), last revised 25 Feb 2026 (this version, v3)] Title:Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models Authors:Aloni Cohen View a PDF of the paper titled Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models, by Aloni Cohen View PDF HTML (experimental) Abstract:Are there any conditions under which a generative model's outputs are guaranteed not to infringe the copyrights of its training data? This is the question of "provable copyright protection" first posed by Vyas, Kakade, and Barak (ICML 2023). They define near access-freeness (NAF) and propose it as sufficient for protection. This paper revisits the question and establishes new foundations for provable copyright protection -- foundations that are firmer both technically and legally. First, we show that NAF alone does not prevent infringement. In fact, NAF models can enable verbatim copying, a blatant failure of copyright protection that we dub being tainted. Then, we introduce our blameless copyright protection framework for defining meaningful guarantees, and instantiate it with clean-room copyright protection. Clean-room copyright protection allows a user to control their risk of copying by behaving in a way that is unlikely to copy in a counterfactual "clean-room setting." Finally, we formalize a common intuition about differential privacy and copyright b...
