Adaptive local adversarial attacks on 3D point clouds

Abstract

Modern artificial intelligence systems rely heavily on deep learning techniques. However, deep neural networks are easily disturbed by adversarial objects. Adversarial examples are beneficial to improve the robustness of the 3D neural network model and enhance the stability of the artificial intelligence system. At present, most 3D adversarial attack methods perturb the entire point cloud to generate adversarial examples, which results in high perturbation costs and low operability in the physical world. In this paper, we propose an adaptive local adversarial attack method (AL-Adv) on 3D point clouds to generate adversarial point clouds. First, we analyze the vulnerability of the 3D network model and extract the salient regions of the input point cloud, namely the vulnerable regions. Second, we propose an adaptive gradient attack algorithm that targets salient regions. The proposed attack algorithm adaptively assigns different disturbances in different directions of the three-dimensional coordinates of the point cloud. Experimental results show that our proposed AL-Adv method achieves a higher attack success rate than the global attack method. Specifically, the adversarial examples generated by AL-Adv demonstrate good imperceptibility and small generation costs.