  In recent years, diffusion models (DMs) have drawn significant attention for
their success in approximating data distributions, yielding state-of-the-art
generative results. Nevertheless, the versatility of these models extends
beyond their generative capabilities to encompass various vision applications,
such as image inpainting, segmentation, adversarial robustness, among others.
This study is dedicated to the investigation of adversarial attacks through the
lens of diffusion models. However, our objective does not involve enhancing the
adversarial robustness of image classifiers. Instead, our focus lies in
utilizing the diffusion model to detect and analyze the anomalies introduced by
these attacks on images. To that end, we systematically examine the alignment
of the distributions of adversarial examples when subjected to the process of
transformation using diffusion models. The efficacy of this approach is
assessed across CIFAR-10 and ImageNet datasets, including varying image sizes
in the latter. The results demonstrate a notable capacity to discriminate
effectively between benign and attacked images, providing compelling evidence
that adversarial instances do not align with the learned manifold of the DMs.
