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 by proving that DP implies clean-room copyright protection when the dataset is golden, a copyright deduplication requirement.

翻译：生成模型的输出是否存在某些条件，能确保其不侵犯训练数据的版权？这正是Vyas、Kakade与Barak（ICML 2023）首次提出的“可证明版权保护”问题。他们定义了近似无访问性（NAF）并将其作为保护的充分条件。本文重新审视该问题，并为可证明版权保护建立了新的理论基础——这些基础在技术与法律层面都更为坚实。首先，我们证明仅凭NAF无法防止侵权。事实上，NAF模型可能促成逐字复制，这种版权保护的明显失败我们称之为污染。随后，我们提出用于定义有效保证的无责版权保护框架，并通过洁净室版权保护实现该框架。洁净室版权保护允许用户通过采取在反事实“洁净室设定”中不太可能复制的行为方式，来控制自身的复制风险。最后，我们通过证明当数据集满足黄金标准（一种版权去重要求）时，差分隐私可蕴含洁净室版权保护，从而形式化了关于差分隐私与版权的普遍直觉。