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Diffusion models have begun to overshadow GANs and other generative models inindustrial applications due to their superior image generation performance. Thecomplex architecture of these models furnishes an extensive array of attackfeatures. In light of this, we aim to design membership inference attacks(MIAs) catered to diffusion models. We first conduct an exhaustive analysis ofexisting MIAs on diffusion models, taking into account factors such asblack-box/white-box models and the selection of attack features. We found thatwhite-box attacks are highly applicable in real-world scenarios, and the mosteffective attacks presently are white-box. Departing from earlier research,which employs model loss as the attack feature for white-box MIAs, we employmodel gradients in our attack, leveraging the fact that these gradients providea more profound understanding of model responses to various samples. We subjectthese models to rigorous testing across a range of parameters, includingtraining steps, sampling frequency, diffusion steps, and data variance. Acrossall experimental settings, our method consistently demonstrated near-flawlessattack performance, with attack success rate approaching $100\%$ and attackAUCROC near $1.0$. We also evaluate our attack against common defensemechanisms, and observe our attacks continue to exhibit commendableperformance.

White-box Membership Inference Attacks against Diffusion Models