  Text-to-Image (T2I) Diffusion Models (DMs) have shown impressive abilities in
generating high-quality images based on simple text descriptions. However, as
is common with many Deep Learning (DL) models, DMs are subject to a lack of
robustness. While there are attempts to evaluate the robustness of T2I DMs as a
binary or worst-case problem, they cannot answer how robust in general the
model is whenever an adversarial example (AE) can be found. In this study, we
first introduce a probabilistic notion of T2I DMs' robustness; and then
establish an efficient framework, ProTIP, to evaluate it with statistical
guarantees. The main challenges stem from: i) the high computational cost of
the generation process; and ii) determining if a perturbed input is an AE
involves comparing two output distributions, which is fundamentally harder
compared to other DL tasks like classification where an AE is identified upon
misprediction of labels. To tackle the challenges, we employ sequential
analysis with efficacy and futility early stopping rules in the statistical
testing for identifying AEs, and adaptive concentration inequalities to
dynamically determine the "just-right" number of stochastic perturbations
whenever the verification target is met. Empirical experiments validate the
effectiveness and efficiency of ProTIP over common T2I DMs. Finally, we
demonstrate an application of ProTIP to rank commonly used defence methods.
