2019/08/16 by Daniel Liu, Ronald Yu, Liu, Daniel +3 · 1 voice · 5 citations
Computer Science · Engineering · Materials Science · Mathematics · #Adversarial Robustness in Machine Learning #Adversarial system #Artificial intelligence #Artificial neural network #Computer science #Computer vision #Deep learning #Drone #Geometry #High-Velocity Impact and Material Behavior #Mathematics #Perspective (graphical) #Point (geometry) #Point cloud #Point distribution model #Preprocessor #cs.CR #cs.CV #cs.LG #eess.IV #stat.ML
paper · pdf · doi:10.48550/arxiv.1908.06062
published in arXiv (Cornell University) (Cornell University) · 18 pages, accepted to the 2020 ECCV workshop on Adversarial Robustness in the Real World, source code available at this https url: https://github.com/Daniel-Liu-c0deb0t/Adversarial-point-perturbations-on-3D-objects
openalex publication_date 2019/08/16 · arxiv created 2020/10/23 · arxiv updated 2020/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.