Loss and Likelihood Based Membership Inference of Diffusion Models

Recent years have witnessed the tremendous success of diffusion models in data synthesis. However, when diffusion models are applied to sensitive data, they also give rise to severe privacy concerns. In this paper, we present a comprehensive study about membership inference attacks against diffusion models, which aims to infer whether a sample was used to train the model. Two attack methods are proposed, namely loss-based and likelihood-based attacks. Our attack methods are evaluated on several state-of-the-art diffusion models, over different datasets in relation to privacy-sensitive data. Extensive experimental evaluations reveal the relationship between membership leakages and generative mechanisms of diffusion models. Furthermore, we exhaustively investigate various factors which can affect membership inference. Finally, we evaluate the membership risks of diffusion models trained with differential privacy.