SC-NET: SALIENT POINT AND CURVATURE BASED ADVERSARIAL POINT CLOUD GENERATION NETWORK

Deep neural networks for 3D point clouds are receiving increasing attention. Recent works have shown that deep neural networks for 3D point clouds are vulnerable to adversarial attacks. However, existing adversarial attacks typically iteratively optimize a single sample to generate the adversarial point cloud, which requires exhausting computations. To make things worse, each point from the point cloud is processed indiscriminately, ignoring distinctions among points. To overcome the shortcomings mentioned above, we propose a method called SC-Net, which can generate an adversarial point cloud in a single forward pass. Specifically, SC-Net treats each point discriminatively by selecting salient points as attack targets. Furthermore, an elaborate Curvature-based distance loss is designed to constrain the strength of attacks to ensure surface consistency. Comparison and ablation experiments demonstrate SC-Net's superior performance.