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.

通过引入概率性的文本到图像扩散模型（T2I DMs）的鲁棒性概念，并建立一个高效的统计性保证评估框架（ProTIP），本研究在解决生成过程的高计算成本和对比两个输出分布以确定扰动输入是否为对抗性示例的困难之后，通过实证实验验证了 ProTIP 在评估鲁棒性和效率方面相较于通用的 T2I DMs 的有效性，并展示了将 ProTIP 应用于常用防御方法排序的应用场景。

ProTIP: 针对随机扰动的文图扩散模型进行概率稳健性验证

ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion  Models against Stochastic Perturbation

With the increasing size of today's data sets, finding the right parameter
configuration in model selection via cross-validation can be an extremely
time-consuming task. In this paper we propose an improved cross-validation
procedure which uses nonparametric testing coupled with sequential analysis to
determine the best parameter set on linearly increasing subsets of the data. By
eliminating underperforming candidates quickly and keeping promising candidates
as long as possible, the method speeds up the computation while preserving the
capability of the full cross-validation. Theoretical considerations underline
the statistical power of our procedure. The experimental evaluation shows that
our method reduces the computation time by a factor of up to 120 compared to a
full cross-validation with a negligible impact on the accuracy.

本文提出了一种基于非参数检验和顺序分析的改良型交叉验证程序，可以在保留完整的交叉验证性能的同时，快速找到最佳参数集，比完整的交叉验证计算时间减少了高达 120 倍，并对准确性影响不大。