  Diffusion models have begun to overshadow GANs and other generative models in
industrial applications due to their superior image generation performance. The
complex architecture of these models furnishes an extensive array of attack
features. In light of this, we aim to design membership inference attacks
(MIAs) catered to diffusion models. We first conduct an exhaustive analysis of
existing MIAs on diffusion models, taking into account factors such as
black-box/white-box models and the selection of attack features. We found that
white-box attacks are highly applicable in real-world scenarios, and the most
effective attacks presently are white-box. Departing from earlier research,
which employs model loss as the attack feature for white-box MIAs, we employ
model gradients in our attack, leveraging the fact that these gradients provide
a more profound understanding of model responses to various samples. We subject
these models to rigorous testing across a range of parameters, including
training steps, sampling frequency, diffusion steps, and data variance. Across
all experimental settings, our method consistently demonstrated near-flawless
attack performance, with attack success rate approaching $100\%$ and attack
AUCROC near $1.0$. We also evaluate our attack against common defense
mechanisms, and observe our attacks continue to exhibit commendable
performance.
