Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
blameless copy protection frameworkclean-room copy protectioncopyright deduplicationcounterfactual settingsdifferential privacygolden datasetlegal foundationsmeaningful guaranteesnafnear access-freenessprovable copyright protectionrisk controltainted modelstechnical foundationsverbatim copying
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