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In recent years, diffusion models have achieved tremendous success in thefield of image generation, becoming the stateof-the-art technology for AI-basedimage processing applications. Despite the numerous benefits brought by recentadvances in diffusion models, there are also concerns about their potentialmisuse, specifically in terms of privacy breaches and intellectual propertyinfringement. In particular, some of their unique characteristics open up newattack surfaces when considering the real-world deployment of such models. Witha thorough investigation of the attack vectors, we develop a systematicanalysis of membership inference attacks on diffusion models and propose novelattack methods tailored to each attack scenario specifically relevant todiffusion models. Our approach exploits easily obtainable quantities and ishighly effective, achieving near-perfect attack performance (>0.9 AUCROC) inrealistic scenarios. Our extensive experiments demonstrate the effectiveness ofour method, highlighting the importance of considering privacy and intellectualproperty risks when using diffusion models in image generation tasks.

Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy