  With wider application of deep neural networks (DNNs) in various algorithms
and frameworks, security threats have become one of the concerns. Adversarial
attacks disturb DNN-based image classifiers, in which attackers can
intentionally add imperceptible adversarial perturbations on input images to
fool the classifiers. In this paper, we propose a novel purification approach,
referred to as guided diffusion model for purification (GDMP), to help protect
classifiers from adversarial attacks. The core of our approach is to embed
purification into the diffusion denoising process of a Denoised Diffusion
Probabilistic Model (DDPM), so that its diffusion process could submerge the
adversarial perturbations with gradually added Gaussian noises, and both of
these noises can be simultaneously removed following a guided denoising
process. On our comprehensive experiments across various datasets, the proposed
GDMP is shown to reduce the perturbations raised by adversarial attacks to a
shallow range, thereby significantly improving the correctness of
classification. GDMP improves the robust accuracy by 5%, obtaining 90.1% under
PGD attack on the CIFAR10 dataset. Moreover, GDMP achieves 70.94% robustness on
the challenging ImageNet dataset.
