Adversarial shape perturbations on 3D point clouds
This addresses security vulnerabilities in 3D vision applications such as robotics and autonomous driving, but is incremental as it builds on prior attack and defense methods.
The paper tackled the problem of creating robust 3D neural networks by exploring adversarial attacks on point clouds, showing that shape attacks can be effective even against existing defenses like point-removal.
The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks in robotics, drone control, and autonomous driving. One commonly used 3D data type is 3D point clouds, which describe shape information. We examine the problem of creating robust models from the perspective of the attacker, which is necessary in understanding how 3D neural networks can be exploited. We explore two categories of attacks: distributional attacks that involve imperceptible perturbations to the distribution of points, and shape attacks that involve deforming the shape represented by a point cloud. We explore three possible shape attacks for attacking 3D point cloud classification and show that some of them are able to be effective even against preprocessing steps, like the previously proposed point-removal defenses.