DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective

Understand

The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets.

• However, the common lack of disclosure regarding their usage raises significant privacy and copyright concerns.
• Dataset auditing techniques, which aim to determine if a specific dataset was used to train a given suspicious model, provide promising solutions to addressing these transparency gaps.
• While prior work has developed various auditing methods, their resilience against dedicated adversarial attacks remains largely unexplored.