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
systematically present the first 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 show that our
attacks can achieve remarkable performance. Furthermore, we exhaustively
investigate various factors which can affect attack performance. Finally, we
also evaluate the performance of our attack methods on diffusion models trained
with differential privacy.

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