Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples. (arXiv:2307.16361v2 [cs.CV] UPDATED)

Deep Neural Networks (DNNs) for 3D point cloud recognition are vulnerable to
adversarial examples, threatening their practical deployment. Despite the many
research endeavors have been made to tackle this issue in recent years, the
diversity of adversarial examples on 3D point clouds makes them more
challenging to defend against than those on 2D images. For examples, attackers
can generate adversarial examples by adding, shifting, or removing points.
Consequently, existing defense strategies are hard to counter unseen point
cloud adversarial examples. …

adversarial benchmarking cloud clouds defending deployment diversity issue networks neural networks point recognition research vulnerable