[2303.06641] Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality

As the key technology of augmented reality (AR), 3D recognition and tracking
are always vulnerable to adversarial examples, which will cause serious
security risks to AR systems. Adversarial examples are beneficial to improve
the robustness of the 3D neural network model and enhance the stability of the
AR system. At present, most 3D adversarial attack methods perturb the entire
point cloud to generate adversarial examples, which results in high
perturbation costs and difficulty in reconstructing the corresponding real
objects 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
vulnerable 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 method AL-Adv
achieves a higher attack success rate than the global attack method.
Specifically, the adversarial examples generated by the AL-Adv demonstrate good
imperceptibility and small generation costs.