Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey
Deep learning has successfully solved a wide range of tasks in 2D vision as a dominant AI technique. Recently, deep learning on 3D point clouds is becoming increasingly popular for addressing various tasks in this field. Despite remarkable achievements, deep learning algorithms are vulnerable to adversarial attacks. These attacks are imperceptible to the human eye but can easily fool deep neural networks in the testing and deployment stage. To encourage future research, this survey summarizes the current progress on adversarial attack and defense techniques on point cloud classification.This paper first introduces the principles and characteristics of adversarial attacks and summarizes and analyzes adversarial example generation methods in recent years. Additionally, it provides an overview of defense strategies, organized into data-focused and model-focused methods. Finally, it presents several current challenges and potential future research directions in this domain.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Point Cloud ClassificationAdversarial AttackDeep LearningPoint Cloud ClassificationSimilar Papers 제목 키워드 기반
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
3D object classification and segmentation using deep neural networks has been extremely successful. As the problem of identifying 3D objects has many safety-critical applications, the neural networks have to be robust ag…
3D Object ClassificationGeneral Classificationimage-classificationImage ClassificationTransferable 3D Adversarial Shape Completion using Diffusion Models
Recent studies that incorporate geometric features and transformers into 3D point cloud feature learning have significantly improved the performance of 3D deep-learning models. However, their robustness against adversari…
3D Point Cloud ClassificationAdversarial AttackPoint Cloud ClassificationOn Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks
3D point clouds play pivotal roles in various safety-critical applications, such as autonomous driving, which desires the underlying deep neural networks to be robust to adversarial perturbations. Though a few defenses a…
3D Point Cloud ClassificationAdversarial RobustnessAutonomous DrivingGeneral Classification+1Defense-PointNet: Protecting PointNet Against Adversarial Attacks
Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks. Many works focus on adversarial attacks and defenses on 2D images, but few focus on 3…
Adversarial RobustnessDUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense
Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose a Denoiser and UPsampler Network (DUP-Net) structure as defenses for 3D adversar…
DenoisingPoint Cloud Classification