  Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious
perturbations, known as adversarial attacks. Following the discovery of this
vulnerability in real-world imaging and vision applications, the associated
safety concerns have attracted vast research attention, and many defense
techniques have been developed. Most of these defense methods rely on
adversarial training (AT) -- training the classification network on images
perturbed according to a specific threat model, which defines the magnitude of
the allowed modification. Although AT leads to promising results, training on a
specific threat model fails to generalize to other types of perturbations. A
different approach utilizes a preprocessing step to remove the adversarial
perturbation from the attacked image. In this work, we follow the latter path
and aim to develop a technique that leads to robust classifiers across various
realizations of threat models. To this end, we harness the recent advances in
stochastic generative modeling, and means to leverage these for sampling from
conditional distributions. Our defense relies on an addition of Gaussian i.i.d
noise to the attacked image, followed by a pretrained diffusion process -- an
architecture that performs a stochastic iterative process over a denoising
network, yielding a high perceptual quality denoised outcome. The obtained
robustness with this stochastic preprocessing step is validated through
extensive experiments on the CIFAR-10 dataset, showing that our method
outperforms the leading defense methods under various threat models.
