Non-Intrusive Copyright Auditing in DNN-based AI Systems: A Lifecycle-Oriented Survey

For deep-learning-based AI systems, copyright risks are now pervasive across the entire lifecycle, rather than being confined to isolated artifacts such as data, models, or outputs. While intrusive audit approaches that modify training pipelines such as watermarking have been actively studied, they require intervention during model training and may harm model performance. This survey focuses on non-intrusive auditing methods based on mechanisms that do not intervene in the state of the involved copyright assets themselves. Specifically, we organize existing work according to the lifecycle stages that these non-intrusive audits reason about, from training data to trained models and downstream generated content. This lifecycle-oriented view reveals four main copyright auditing tasks: (i) data usage audit, (ii) model derivation audit, (iii) model usage audit, and (iv) data derivation audit. For each task, we provide a structured review of representative approaches and discuss their core contributions. Across these tasks, existing methods can be further categorized into two complementary methodological families: statistical auditing, which estimates the likelihood of copyright-relevant misuse through measurable statistical discrepancies, and evidentiary auditing, which constructs structured technical evidence that may support further assessment of copyright claims. Overall, this survey provides a structured framework for understanding and interpreting non-intrusive copyright auditing methods for different auditing tasks across the AI system lifecycle.