  Deep 3D point cloud models are sensitive to adversarial attacks, which poses
threats to safety-critical applications such as autonomous driving. Robust
training and defend-by-denoising are typical strategies for defending
adversarial perturbations. However, they either induce massive computational
overhead or rely heavily upon specified priors, limiting generalized robustness
against attacks of all kinds. To remedy it, this paper introduces a novel
distortion-aware defense framework that can rebuild the pristine data
distribution with a tailored intensity estimator and a diffusion model. To
perform distortion-aware forward diffusion, we design a distortion estimation
algorithm that is obtained by summing the distance of each point to the
best-fitting plane of its local neighboring points, which is based on the
observation of the local spatial properties of the adversarial point cloud. By
iterative diffusion and reverse denoising, the perturbed point cloud under
various distortions can be restored back to a clean distribution. This approach
enables effective defense against adaptive attacks with varying noise budgets,
enhancing the robustness of existing 3D deep recognition models.
