Compressive imaging for thwarting adversarial attacks on 3D point cloud classifiers

Abstract

Three dimensional (3D) point cloud classifiers are used extensively in safety crucial applications such as autonomous cars, face recognition, military applications, and many more. Despite the critical importance of their reliability, 3D classifiers are prone to adversarial attacks that can be crafted in the real world. While it is possible to use known methods to prevent adversarial attacks, they can be easily counter-attacked, leading to an arms race between the attacker and the defender. Here, we propose to use 3D compressive sensing to recover an original label of the 3D object. Since compressive sensing inherently encodes the 3D signal, it also prevents the arms race between the attacker and the defender. The 3D compressive sensing we consider is a single pixel camera (SPC) system that can be implemented in Light Detection and Ranging (LiDAR) systems.