Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving
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Abstract (arXiv)
Our study assesses the adversarial robustness of LiDAR-camera fusion models in 3D object detection. We introduce an attack technique that, by simply adding a limited number of physically constrained adversarial points above a car, can make the car undetectable by the fusion model. Experimental results reveal that even without changes to the image data channel, the fusion model can be deceived solely by manipulating the LiDAR data channel. This finding raises safety concerns in the field of autonomous driving. Further, we explore how the quantity of adversarial points, the distance between the front-near car and the LiDAR-equipped car, and various angular factors affect the attack success rate. We believe our research can contribute to the understanding of multi-sensor robustness, offering insights and guidance to enhance the safety of autonomous driving.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 3 Dec 2023.
Builds on
17 of 24 references.
References · 17
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- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
- 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
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- 2020EPNet: Enhancing Point Features with Image Semantics for 3D Object Detectioncited by 164
- 2019MVX-Net: Multimodal VoxelNet for 3D Object Detectioncited by 162
- 2019Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Drivingcited by 151
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- 2019Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detectioncited by 124
- 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
- 2020Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasurescited by 95
5 more
- 2020Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attackcited by 64
- 2021Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Drivingcited by 45
- 2021Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehiclescited by 26
- 2021Adversarial Attacks on Camera-LiDAR Models for 3D Car Detectioncited by 16
- 2021Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Modelscited by 5
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