SynerGuard: A Robust Framework for Point Cloud Classification via Local Geometry and Spatial Topology

Point cloud recognition models are known to be vulnerable to adversarial attacks. The state-of-the-art defense solutions either focus on partial features of the point cloud, limiting their effectiveness, or rely heavily on known adversarial examples, reducing their generalizability, while others, like point cloud reconstruction, will degrade the classifier's accuracy on clean examples. To address this, we introduce SynerGuard, a novel robust point cloud classification framework mitigating adversarial attacks by considering comprehensive geometric and topological attributes of the point cloud, without relying on known adversarial examples while attaining classification accuracies on clean examples. We comprehensively test SynerGuard against seven attack types from three leading adversarial attack approaches on two widely used datasets, ModelNet40 and ShapeNetPart. The results demonstrate SynERGUARD's superiority against existing defenses in mitigating adversarial attacks, as well as managing clean examples.