Deep neural networks (DNNs) are increasingly integrated into LiDAR (Light Detection and Ranging)-based perception systems for autonomous vehicles (AVs), requiring robust performance under adversarial conditions. We aim to address the challenge of LiDAR spoofing attacks, where attackers inject fake objects into LiDAR data and fool AVs to misinterpret their environment and make erroneous decisions. However, current defense algorithms predominantly depend on perception outputs (i.e., bounding boxes) thus face limitations in detecting attackers given the bounding boxes are generated by imperfect perception models processing limited points, acquired based on the ego vehicle's viewpoint. To overcome these limitations, we propose a novel framework, named ADoPT (Anomaly Detection based on Point-level Temporal consistency), which quantitatively measures temporal consistency across consecutive frames and identifies abnormal objects based on the coherency of point clusters. In our evaluation using the nuScenes dataset, our algorithm effectively counters various LiDAR spoofing attacks, achieving a low (< 10%) false positive ratio (FPR) and high (> 85%) true positive ratio (TPR), outperforming existing state-of-the-art defense methods, CARLO and 3D-TC2. Furthermore, our evaluation demonstrates the promising potential for accurate attack detection across various road environments.

通过量化连续帧之间的时间一致性并基于点簇的连贯性来识别异常对象，我们提出了一个名为ADoPT（基于点级时间一致性的异常检测）的新框架，优于现有的最先进的防御方法CARLO和3D-TC2，有效地对抗各种LiDAR欺骗攻击，在各种道路环境中实现低（<10％）误报率（FPR）和高（> 85％）真阳率（TPR），同时证明了准确的攻击检测的潜力。

基于点级时态一致性的LiDAR欺骗攻击检测ADoPT