Recently, several attempts have demonstrated that 3D deep learning models are as
vulnerable to adversarial example attacks as 2D models. However, these methods are still …

With the maturity of depth sensors, the vulnerability of 3D point cloud models has received
increasing attention in various applications such as autonomous driving and robot …

Adversarial attacks have been successfully extended to the field of point clouds. Besides
applying the common perturbation guided by the gradient, adversarial attacks on point …

Adversarial attacks on 3D point clouds often exhibit unsatisfactory imperceptibility, which
primarily stems from the disregard for manifold-aware distortion, ie, distortion of the …

ABSTRACT Three-dimensional (3D) point clouds (PCs) and meshes have increasingly
become available and indispensable for diversified applications in work and life. In addition …

Adversarial attack on point clouds plays a vital role in evaluating and improving the
adversarial robustness of 3D deep learning models. Current attack methods are mainly …

Point cloud classification is an essential component in many security-critical applications
such as autonomous driving and augmented reality. However, point cloud classifiers are …

Adversarial attacks on point clouds play a vital role in assessing and enhancing the
adversarial robustness of 3D deep learning models. While employing a variety of geometric …