3D point clouds play pivotal roles in various safety-critical applications,
such as autonomous driving, which desires the underlying deep neural networks
to be robust to adversarial perturbations. Though a few defenses against
adversarial point cloud classification have been proposed, it remains unknown
whether they are truly robust to adaptive attacks. To this end, we perform the
first security analysis of state-of-the-art defenses and design adaptive
evaluations on them. Our 100% adaptive attack success rates show that current
countermeasures are still vulnerable. Since adversarial training (AT) is
believed as the most robust defense, we present the first in-depth study
showing how AT behaves in point cloud classification and identify that the
required symmetric function (pooling operation) is paramount to the 3D model’s
robustness under AT. Through our systematic analysis, we find that the
default-used fixed pooling (e.g., MAX pooling) generally weakens AT’s
effectiveness in point cloud classification. Interestingly, we further discover
that sorting-based parametric pooling can significantly improve the models’
robustness. Based on above insights, we propose DeepSym, a deep symmetric
pooling operation, to architecturally advance the robustness to 47.0% under AT
without sacrificing nominal accuracy, outperforming the original design and a
strong baseline by 28.5% ($sim 2.6 times$) and 6.5%, respectively, in

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