  Image reconstruction attacks on machine learning models pose a significant
risk to privacy by potentially leaking sensitive information. Although
defending against such attacks using differential privacy (DP) has proven
effective, determining appropriate DP parameters remains challenging. Current
formal guarantees on data reconstruction success suffer from overly theoretical
assumptions regarding adversary knowledge about the target data, particularly
in the image domain. In this work, we empirically investigate this discrepancy
and find that the practicality of these assumptions strongly depends on the
domain shift between the data prior and the reconstruction target. We propose a
reconstruction attack based on diffusion models (DMs) that assumes adversary
access to real-world image priors and assess its implications on privacy
leakage under DP-SGD. We show that (1) real-world data priors significantly
influence reconstruction success, (2) current reconstruction bounds do not
model the risk posed by data priors well, and (3) DMs can serve as effective
auditing tools for visualizing privacy leakage.
