  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.
