  In recent years, diffusion models have achieved tremendous success in the
field of image generation, becoming the stateof-the-art technology for AI-based
image processing applications. Despite the numerous benefits brought by recent
advances in diffusion models, there are also concerns about their potential
misuse, specifically in terms of privacy breaches and intellectual property
infringement. In particular, some of their unique characteristics open up new
attack surfaces when considering the real-world deployment of such models. With
a thorough investigation of the attack vectors, we develop a systematic
analysis of membership inference attacks on diffusion models and propose novel
attack methods tailored to each attack scenario specifically relevant to
diffusion models. Our approach exploits easily obtainable quantities and is
highly effective, achieving near-perfect attack performance (>0.9 AUCROC) in
realistic scenarios. Our extensive experiments demonstrate the effectiveness of
our method, highlighting the importance of considering privacy and intellectual
property risks when using diffusion models in image generation tasks.
