arXiv:2606.31991v1 Announce Type: new Abstract: The tendency of large generative models to memorize training data makes sample verification critical for privacy auditing and copyright enforcement. Current membership (MIA) and dataset inference (DI) attacks often rely on one-shot generations, which yield weak signals and limited sensitivity across modalities. Inspired by Model Autophagy Disorder (MAD), we introduce MADreMIA, a model-agnostic framework that enhances white-, gray-, and black-box MIA and DI. Rather than relying on shadow model training -- often infeasible for large generative mode
The proliferation of large generative models makes the lack of robust membership inference techniques a pressing issue for privacy and intellectual property as these models become more embedded in commercial applications.
Improved membership inference directly impacts the privacy and copyright posture of organizations using or developing AI, enabling more rigorous auditing and enforcement.
The ability to accurately detect data memorization in generative AI models shifts the landscape for auditing, intellectual property protection, and privacy compliance.
- · Privacy auditors
- · Generative AI users (data privacy)
- · Content creators (copyright enforcement)
- · Academic researchers (MIA/DI)
- · Malicious actors (data exfiltration)
- · Developers ignoring data privacy regulations
- · Models reliant on uncleared training data
Research into Model Autophagy Disorder (MAD) leads to a novel framework, MADreMIA, significantly enhancing membership inference robustness across model types.
The improved detection of memorized data instigates stricter data governance and curation practices for training large generative AI models.
Increased legal challenges and settlements related to data privacy and copyright infringement against companies utilizing large generative models.
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Read at arXiv cs.LG