Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

Article


Mahima, K. T. Yasas, Perera, Asanka, Anavatti, Sreenatha and Garratt, Matt. 2023. "Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving." Sensors. 23 (23). https://doi.org/10.3390/s23239579
Article Title

Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

ERA Journal ID34304
Article CategoryArticle
AuthorsMahima, K. T. Yasas, Perera, Asanka, Anavatti, Sreenatha and Garratt, Matt
Journal TitleSensors
Journal Citation23 (23)
Article Number9579
Number of Pages22
Year2023
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1424-8220
1424-8239
Digital Object Identifier (DOI)https://doi.org/10.3390/s23239579
Web Address (URL)https://www.mdpi.com/1424-8220/23/23/9579
Abstract

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.

Keywordsadversarial attacks; LiDAR; semantic segmentation; autonomous vehicles
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204007. Control engineering, mechatronics and robotics
Byline AffiliationsUniversity of New South Wales
School of Engineering
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