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As the key technology of augmented reality (AR), 3D recognition and trackingare always vulnerable to adversarial examples, which will cause serioussecurity risks to AR systems. Adversarial examples are beneficial to improvethe robustness of the 3D neural network model and enhance the stability of theAR system. At present, most 3D adversarial attack methods perturb the entirepoint cloud to generate adversarial examples, which results in highperturbation costs and difficulty in reconstructing the corresponding realobjects in the physical world. In this paper, we propose an adaptive localadversarial attack method (AL-Adv) on 3D point clouds to generate adversarialpoint clouds. First, we analyze the vulnerability of the 3D network model andextract the salient regions of the input point cloud, namely the vulnerableregions. Second, we propose an adaptive gradient attack algorithm that targetsvulnerable regions. The proposed attack algorithm adaptively assigns differentdisturbances in different directions of the three-dimensional coordinates ofthe point cloud. Experimental results show that our proposed method AL-Advachieves a higher attack success rate than the global attack method.Specifically, the adversarial examples generated by the AL-Adv demonstrate goodimperceptibility and small generation costs.

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