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Auditing Patient Privacy in Medical Generative Models: Scalable Memorization Detection with DeepSSIM++

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

While deep generative models offer new opportunities for medical image synthesis and data sharing, their ability to memorize and reproduce training samples raises serious concerns about patient confidentiality. Detecting such memorization at scale remains challenging: traditional pixel-based metrics are sensitive to generation artifacts, whereas generic embedding-based metrics often lack the anatomical sensitivity required for medical data. To address this challenge, we introduce DeepSSIM++, a self-supervised similarity metric for scalable memorization auditing in medical generative models. By

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