SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909564744499200 |
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| author | Kumar, Prashant Vattikonda, Dheeraj Bhat, Kshitij Madhav Dargan, Kunal Kalra, Prem |
| author_facet | Kumar, Prashant Vattikonda, Dheeraj Bhat, Kshitij Madhav Dargan, Kunal Kalra, Prem |
| contents | The widespread adoption of learning-based methods for the LiDAR makes autonomous vehicles vulnerable to adversarial attacks through adversarial \textit{point injections (PiJ)}. It poses serious security challenges for navigation and map generation. Despite its critical nature, no major work exists that studies learning-based attacks on LiDAR-based SLAM. Our work proposes SLACK, an end-to-end deep generative adversarial model to attack LiDAR scans with several point injections without deteriorating LiDAR quality. To facilitate SLACK, we design a novel yet simple autoencoder that augments contrastive learning with segmentation-based attention for precise reconstructions. SLACK demonstrates superior performance on the task of \textit{point injections (PiJ)} compared to the best baselines on KITTI and CARLA-64 dataset while maintaining accurate scan quality. We qualitatively and quantitatively demonstrate PiJ attacks using a fraction of LiDAR points. It severely degrades navigation and map quality without deteriorating the LiDAR scan quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03089 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections Kumar, Prashant Vattikonda, Dheeraj Bhat, Kshitij Madhav Dargan, Kunal Kalra, Prem Computer Vision and Pattern Recognition The widespread adoption of learning-based methods for the LiDAR makes autonomous vehicles vulnerable to adversarial attacks through adversarial \textit{point injections (PiJ)}. It poses serious security challenges for navigation and map generation. Despite its critical nature, no major work exists that studies learning-based attacks on LiDAR-based SLAM. Our work proposes SLACK, an end-to-end deep generative adversarial model to attack LiDAR scans with several point injections without deteriorating LiDAR quality. To facilitate SLACK, we design a novel yet simple autoencoder that augments contrastive learning with segmentation-based attention for precise reconstructions. SLACK demonstrates superior performance on the task of \textit{point injections (PiJ)} compared to the best baselines on KITTI and CARLA-64 dataset while maintaining accurate scan quality. We qualitatively and quantitatively demonstrate PiJ attacks using a fraction of LiDAR points. It severely degrades navigation and map quality without deteriorating the LiDAR scan quality. |
| title | SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.03089 |