LiDAttack: Robust Black-box Attack on LiDAR-based Object Detection
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%.
Code (1)
Tasks
Adversarial Attackobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
ScAR: Scaling Adversarial Robustness for LiDAR Object Detection
The adversarial robustness of a model is its ability to resist adversarial attacks in the form of small perturbations to input data. Universal adversarial attack methods such as Fast Sign Gradient Method (FSGM) and Proje…
3D Object DetectionAdversarial AttackAdversarial RobustnessObject+2Mirage: a Clean-Label Backdoor against LiDAR 3D Object Detection
Deep neural network-based LiDAR 3D object detection serves as a critical perception component in safety-critical autonomous systems. However, recent studies have revealed its vulnerability to backdoor attacks. Existing a…
3D Object DetectionTowards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures
Perception plays a pivotal role in autonomous driving systems, which utilizes onboard sensors like cameras and LiDARs (Light Detection and Ranging) to assess surroundings. Recent studies have demonstrated that LiDAR-base…
Autonomous DrivingSelf-Driving CarsATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception
Autonomous driving perception is typically evaluated on clean benchmark data, yet real-world deployment requires robustness to rare, structured, and potentially adversarial sensor anomalies. This gap is especially critic…
Autonomous DrivingSecurity Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehicles
To enable safe and reliable decision-making, autonomous vehicles (AVs) feed sensor data to perception algorithms to understand the environment. Sensor fusion with multi-frame tracking is becoming increasingly popular for…
Autonomous VehiclesDecision MakingSensor Fusion