A Survey of Adversarial Attacks on 3D Point Cloud Object Recognition
Currently,artificial intelligence systems have achieved significant success in various domains,with deep learning technology playing a pivotal role.However,although the deep neural network has strong inference recognition ability,it is still vulnerable to the attack of adversarial examples,showing its vulnerability.Adversarial samples are specially crafted input data designed to attack and mislead the outputs of deep learning models.With the rapid development of 3D sensors such as LiDAR,the use of deep learning technology to address various intelligent tasks in the 3D domain is gaining increasing attention.Ensuring the security and robustness of artificial intelligence systems that process 3D point cloud data,such as deep learning-based autonomous 3D object detection and recognition for self-driving vehicles,is crucial.In order to analyze the methods by which 3D adversarial samples attack deep neural networks,and reveal the interference mechanisms of 3D adversarial samples on deep neural networks,this paper summarizes the research progress on adversarial attack methods for deep neural network models based on 3D point cloud data.The paper first introduces the fundamental principles and implementation methods of adversarial attacks,and then it summarizes and analyzes digital domain adversarial attacks and physical domain adversarial attacks on 3D point clouds.Finally,it discusses the challenges and future research directions in the realm of 3D point cloud adversarial attacks.
Adversarial attackDeep learning3D point cloudAdversarial examples
万方数据
维普
