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Although 3D point cloud classification has recently been widely deployed indifferent application scenarios, it is still very vulnerable to adversarialattacks. This increases the importance of robust training of 3D models in theface of adversarial attacks. Based on our analysis on the performance ofexisting adversarial attacks, more adversarial perturbations are found in themid and high-frequency components of input data. Therefore, by suppressing thehigh-frequency content in the training phase, the models robustness againstadversarial examples is improved. Experiments showed that the proposed defensemethod decreases the success rate of six attacks on PointNet, PointNet++ ,, andDGCNN models. In particular, improvements are achieved with an average increaseof classification accuracy by 3.8 % on drop100 attack and 4.26 % on drop200attack compared to the state-of-the-art methods. The method also improvesmodels accuracy on the original dataset compared to other available methods.

LPF-Defense: 3D Adversarial Defense based on Frequency Analysis