Explainability-Aware One Point Attack for Point Cloud Neural Networks

Tan, Hanxiao; Kotthaus, Helena

doi:10.1109/wacv56688.2023.00456

Cited by 2 publications

(1 citation statement)

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“…In some cases, the attack can fool the classifier by changing a single point when the point has a large distance from the surface of the objects. Analogously to the one-pixel attack for images [83], Tan et al [84] proposed an attack called One point attack in which only a single point in the point cloud needs to be shifted in order to fool the deep model. The authors also present a method to identify the most important points in the point cloud based on a saliency map, which could be used as candidates for the attack.…”

Section: ) Point Shift Attacksmentioning

confidence: 99%

Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey

Naderi,

Bajić

2023

IEEE Access

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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 has become 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. INDEX TERMS 3D deep learning, deep neural network, adversarial examples, adversarial defense, machine learning security, 3D point clouds.

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Section: ) Point Shift Attacksmentioning

confidence: 99%