MADreMIA

MADreMIA: Chained Regeneration Framework for Amplifying Membership Inference Signals

Researchers introduce MADreMIA, a model-agnostic framework for membership inference attacks (MIA) and dataset inference (DI) that uses iterative chained regeneration across modalities rather than shadow model training. The key insight is that memorized training samples exhibit higher coherence and slower degradation under repeated regeneration than non-member samples, yielding stronger membership signals at low false positive rates. The framework is evaluated across image autoregressive models, diffusion models, language models, and audio models, supporting white-, gray-, and black-box threat models. This work advances privacy auditing and copyright enforcement capabilities for large generative models.