PCV: A Point Cloud-Based Network Verifier
Arup Kumar Sarker, Farzana Yasmin Ahmad, Matthew B. Dwyer

TL;DR
This paper introduces PCV, a novel verifier for 3D point cloud networks like PointNet, which assesses robustness against adversarial attacks by generating and analyzing perturbed inputs.
Contribution
The paper presents the first point cloud-based network verifier capable of layer-wise robustness verification for 3D classifiers like PointNet.
Findings
PointNet's robustness is compromised by hybrid reverse signed adversarial attacks.
The verifier effectively measures accuracy impact under various perturbations.
Hybrid reverse signed attack significantly affects PointNet's resilience.
Abstract
3D vision with real-time LiDAR-based point cloud data became a vital part of autonomous system research, especially perception and prediction modules use for object classification, segmentation, and detection. Despite their success, point cloud-based network models are vulnerable to multiple adversarial attacks, where the certain factor of changes in the validation set causes significant performance drop in well-trained networks. Most of the existing verifiers work perfectly on 2D convolution. Due to complex architecture, dimension of hyper-parameter, and 3D convolution, no verifiers can perform the basic layer-wise verification. It is difficult to conclude the robustness of a 3D vision model without performing the verification. Because there will be always corner cases and adversarial input that can compromise the model's effectiveness. In this project, we describe a point…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdversarial Robustness in Machine Learning
MethodsTest
