Toward Robust 3D Perception for Autonomous Vehicles: A Review of Adversarial Attacks and Countermeasures

Yasas Mahima, K. T.; Perera, Asanka G.; Anavatti, Sreenatha; Garratt, Matt

doi:10.1109/tits.2024.3456293

IEEE Trans. Intell. Transport. Syst.

2024

DOI: 10.1109/tits.2024.3456293

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Toward Robust 3D Perception for Autonomous Vehicles: A Review of Adversarial Attacks and Countermeasures

K. T. Yasas Mahima,

Asanka G. Perera,

Sreenatha Anavatti

et al.

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Cited by 1 publication

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Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

Mahima,

Perera,

Anavatti

et al. 2023

Sensors

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Deep learning networks have demonstrated outstanding performance in 2D and 3D vision tasks. However, recent research demonstrated that these networks result in failures when imperceptible perturbations are added to the input known as adversarial attacks. This phenomenon has recently received increased interest in the field of autonomous vehicles and has been extensively researched on 2D image-based perception tasks and 3D object detection. However, the adversarial robustness of 3D LiDAR semantic segmentation in autonomous vehicles is a relatively unexplored topic. This study expands the adversarial examples to LiDAR-based 3D semantic segmentation. We developed and analyzed three LiDAR point-based adversarial attack methods on different networks developed on the SemanticKITTI dataset. The findings illustrate that the Cylinder3D network has the highest adversarial susceptibility to the analyzed attacks. We investigated how the class-wise point distribution influences the adversarial robustness of each class in the SemanticKITTI dataset and discovered that ground-level points are extremely vulnerable to point perturbation attacks. Further, the transferability of each attack strategy was assessed, and we found that networks relying on point data representation demonstrate a notable level of resistance. Our findings will enable future research in developing more complex and specific adversarial attacks against LiDAR segmentation and countermeasures against adversarial attacks.

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Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

Mahima,

Perera,

Anavatti

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

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Toward Robust 3D Perception for Autonomous Vehicles: A Review of Adversarial Attacks and Countermeasures

Cited by 1 publication

References 174 publications

Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

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