Explainability-Aware One Point Attack for Point Cloud Neural Networks

TL;DR

This paper introduces explainability-aware adversarial attacks on point cloud neural networks, demonstrating their vulnerability to minimal perturbations and exploring the role of critical points in model operation and robustness.

Contribution

It proposes two novel attack methods that leverage explainability to understand and exploit the sensitivity of point cloud networks to critical point perturbations.

Findings

Point cloud networks can be deceived with nearly 100% success by moving a single point.

Different point attribution distributions significantly affect network robustness.

The approach advances understanding of model operation and explainability in 3D deep learning.

Abstract

With the proposition of neural networks for point clouds, deep learning has started to shine in the field of 3D object recognition while researchers have shown an increased interest to investigate the reliability of point cloud networks by adversarial attacks. However, most of the existing studies aim to deceive humans or defense algorithms, while the few that address the operation principles of the models themselves remain flawed in terms of critical point selection. In this work, we propose two adversarial methods: One Point Attack (OPA) and Critical Traversal Attack (CTA), which incorporate the explainability technologies and aim to explore the intrinsic operating principle of point cloud networks and their sensitivity against critical points perturbations. Our results show that popular point cloud networks can be deceived with almost $100\%$ success rate by shifting only one point from the input instance. In addition, we show the interesting impact of different point attribution distributions on the adversarial robustness of point cloud networks. Finally, we discuss how our approaches facilitate the explainability study for point cloud networks. To the best of our knowledge, this is the first point-cloud-based adversarial approach concerning explainability. Our code is available at https://github.com/Explain3D/Exp-One-Point-Atk-PC.