Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving
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 increas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/23/9579 |
| _version_ | 1797399536953458688 |
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| author | K. T. Yasas Mahima Asanka Perera Sreenatha Anavatti Matt Garratt |
| author_facet | K. T. Yasas Mahima Asanka Perera Sreenatha Anavatti Matt Garratt |
| author_sort | K. T. Yasas Mahima |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T01:41:39Z |
| format | Article |
| id | doaj.art-028113a5be4b407e9c7efe1ccbe84ef8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:41:39Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-028113a5be4b407e9c7efe1ccbe84ef82023-12-08T15:26:29ZengMDPI AGSensors1424-82202023-12-012323957910.3390/s23239579Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous DrivingK. T. Yasas Mahima0Asanka Perera1Sreenatha Anavatti2Matt Garratt3School of Engineering and Technology, University of New South Wales, Canberra, ACT 2612, AustraliaSchool of Engineering, University of Southern Queensland, Brisbane, QLD 4300, AustraliaSchool of Engineering and Technology, University of New South Wales, Canberra, ACT 2612, AustraliaSchool of Engineering and Technology, University of New South Wales, Canberra, ACT 2612, AustraliaDeep 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.https://www.mdpi.com/1424-8220/23/23/9579adversarial attacksLiDARsemantic segmentationautonomous vehicles |
| spellingShingle | K. T. Yasas Mahima Asanka Perera Sreenatha Anavatti Matt Garratt Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving Sensors adversarial attacks LiDAR semantic segmentation autonomous vehicles |
| title | Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving |
| title_full | Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving |
| title_fullStr | Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving |
| title_full_unstemmed | Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving |
| title_short | Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving |
| title_sort | exploring adversarial robustness of lidar semantic segmentation in autonomous driving |
| topic | adversarial attacks LiDAR semantic segmentation autonomous vehicles |
| url | https://www.mdpi.com/1424-8220/23/23/9579 |
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