SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections

• URL: http://arxiv.org/abs/2504.03089v1
• Date: Thu, 03 Apr 2025 23:52:49 GMT
• Title: SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections
• Authors: Prashant Kumar, Dheeraj Vattikonda, Kshitij Madhav Bhat, Kunal Dargan, Prem Kalra,
• Abstract summary: No major work exists that studies learning-based attacks on LiDAR-based SLAM.<n>Our work proposes SLACK, an end-to-end deep generative adversarial model to attack LiDAR scans with several point injections without deteriorating LiDAR quality.
• Score: 1.6567468717846674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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