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Deep neural networks (DNNs) have been shown to be vulnerable to adversarialattacks. Recently, 3D adversarial attacks, especially adversarial attacks onpoint clouds, have elicited mounting interest. However, adversarial pointclouds obtained by previous methods show weak transferability and are easy todefend. To address these problems, in this paper we propose a novel point cloudattack (dubbed AOF) that pays more attention on the low-frequency component ofpoint clouds. We combine the losses from point cloud and its low-frequencycomponent to craft adversarial samples. Extensive experiments validate that AOFcan improve the transferability significantly compared to state-of-the-art(SOTA) attacks, and is more robust to SOTA 3D defense methods. Otherwise,compared to clean point clouds, adversarial point clouds obtained by AOFcontain more deformation than outlier.

Boosting 3D Adversarial Attacks with Attacking On Frequency