Membership Inference Attacks on Diffusion Models via Quantile Regression
Shuai Tang Zhiwei Steven Wu Sergul Aydore Michael Kearns Aaron Roth
Recently, diffusion models have become popular tools for image synthesis
because of their high-quality outputs. However, like other large-scale models,
they may leak private information about their training data. Here, we
demonstrate a privacy vulnerability of diffusion models through a
\emph{membership inference (MI) attack}, which aims to identify whether a
target example belongs to the training set when given the trained diffusion
model. Our proposed MI attack learns quantile regression models that predict (a
quantile of) the distribution of reconstruction loss on examples not used in
training. This allows us to define a granular hypothesis test for determining
the membership of a point in the training set, based on thresholding the
reconstruction loss of that point using a custom threshold tailored to the
example. We also provide a simple bootstrap technique that takes a majority
membership prediction over ``a bag of weak attackers'' which improves the
accuracy over individual quantile regression models. We show that our attack
outperforms the prior state-of-the-art attack while being substantially less
computationally expensive -- prior attacks required training multiple ``shadow
models'' with the same architecture as the model under attack, whereas our
attack requires training only much smaller models.