PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition. (arXiv:2208.09801v1 [cs.CV])

3D Point cloud is becoming a critical data representation in many real-world
applications like autonomous driving, robotics, and medical imaging. Although
the success of deep learning further accelerates the adoption of 3D point
clouds in the physical world, deep learning is notorious for its vulnerability
to adversarial attacks. In this work, we first identify that the
state-of-the-art empirical defense, adversarial training, has a major
limitation in applying to 3D point cloud models due to gradient obfuscation. We
further propose PointDP, …

3d adversarial attacks cloud recognition