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 …

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 …

Adversarial attacks on point clouds are crucial for assessing and improving the adversarial
robustness of 3D deep learning models. Despite leveraging various geometric constraints …

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

3D object detection based on deep neural networks (DNNs) has widely been adopted in the
field of embedded applications, such as autonomous driving. Nonetheless, recent studies …

Adversarial attacks on point clouds are crucial for assessing and improving the adversarial
robustness of 3D deep learning models. Traditional solutions strictly limit point displacement …

Utilizing 3D point cloud data has become an urgent need for the deployment of artificial
intelligence in many areas like facial recognition and self-driving. However, deep learning …