mlsanityData to 14 Oct 2026

LiDAttack: Robust Black-box Attack on LiDAR-based Object Detection

arXiv 2411.01889 ↗First posted 4 Nov 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

Since DNN is vulnerable to carefully crafted adversarial examples, adversarial attack on LiDAR sensors have been extensively studied. We introduce a robust black-box attack dubbed LiDAttack. It utilizes a genetic algorithm with a simulated annealing strategy to strictly limit the location and number of perturbation points, achieving a stealthy and effective attack. And it simulates scanning deviations, allowing it to adapt to dynamic changes in real world scenario variations. Extensive experiments are conducted on 3 datasets (i.e., KITTI, nuScenes, and self-constructed data) with 3 dominant object detection models (i.e., PointRCNN, PointPillar, and PV-RCNN++). The results reveal the efficiency of the LiDAttack when targeting a wide range of object detection models, with an attack success rate (ASR) up to 90%.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 48 of its 66 references that are in the map. First posted on arXiv 4 Nov 2024.

Builds on

48 of 66 references.

References · 48

  1. 2013Intriguing properties of neural networkscited by 5,155
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  3. 2016Adversarial examples in the physical worldcited by 1,769
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  5. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  6. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  7. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
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  12. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 326
36 more
  1. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  2. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  3. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  4. 2018PU-Net: Point Cloud Upsampling Networkcited by 209
  5. 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
  6. 20203D Object Detection with Pointformercited by 194
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  8. 2019Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Drivingcited by 152
  9. 2018Generating 3D Adversarial Point Cloudscited by 144
  10. 2020Physically Realizable Adversarial Examples for LiDAR Object Detectioncited by 137
  11. 2020Pillar-based Object Detection for Autonomous Drivingcited by 137
  12. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  13. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  14. 2019Fast Point R-CNNcited by 119
  15. 2018PointCloud Saliency Mapscited by 112
  16. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  17. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  18. 2020Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasurescited by 95
  19. 2019Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifierscited by 92
  20. 2021Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detectioncited by 79
  21. 2019Adversarial Attack and Defense on Point Setscited by 63
  22. 2019Adversarial Objects Against LiDAR-Based Autonomous Driving Systemscited by 63
  23. 2019Robustness of 3D Deep Learning in an Adversarial Settingcited by 55
  24. 2018IPOD: Intensive Point-based Object Detector for Point Cloudcited by 46
  25. 2021Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Drivingcited by 45
  26. 2020Minimal Adversarial Examples for Deep Learning on 3D Point Cloudscited by 41
  27. 2023FastPillars: A Deployment-friendly Pillar-based 3D Detectorcited by 39
  28. 2022You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworkscited by 32
  29. 2020ShapeAdv: Generating Shape-Aware Adversarial 3D Point Cloudscited by 20
  30. 2021Object Removal Attacks on LiDAR-based 3D Object Detectorscited by 16
  31. 2021Adversarial Attacks on Camera-LiDAR Models for 3D Car Detectioncited by 16
  32. 2021Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Modelscited by 14
  33. 2022Li3DeTr: A LiDAR based 3D Detection Transformercited by 11
  34. 2021Adversarial Attack by Limited Point Cloud Surface Modificationscited by 6
  35. 2023Exorcising ''Wraith'': Protecting LiDAR-based Object Detector in Automated Driving System from Appearing Attackscited by 3
  36. 2018DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defensecited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Comprehensive Robustness Analysis of LiDAR-based 3D Object Detection in Autonomous Drivingcited by 0