Autonomous vehicles (AVs) rely on multi-sensor perception, particularly camera and LiDAR systems, for critical tasks such as object detection and scene understanding. However, recent studies have revealed that camera-based perception is especially vulnerable to laser-based adversarial attacks, including the Adversarial Laser Beam (AdvLB) and Adversarial Laser Spot (AdvLS). These attacks are impractical for real-time deployment as they are originally designed with iterative and dynamic configurations. In this work, we introduce and analyze a static-parameter attack strategy targeting the camera, which eliminates the need for iterative tuning while consistently degrading perception accuracy. This simplicity enables realtime feasibility, posing a practical threat to deployed AV systems. We characterize the visual artifacts introduced by such attacks using first-order statistical metrics, image filtering techniques, and machine learning-based anomaly detection. Our results show that even static laser interference can impair object recognition and disrupt camera-LiDAR sensor fusion. Experimental evaluations demonstrate that basic filters such as sharpening and Laplacian, combined with Hue, Saturation, and Value (HSV)-based contour extraction, effectively detect AdvLB artifacts. In contrast, the more subtle AdvLS perturbations require geometry-aware, adaptive detection methods. To address both, we propose and implement a lightweight, unified detection framework that integrates statistical and geometric cues. The system achieves over 90% detection accuracy with near real-time performance on the Jetson Orin Nano device.
Solanki et al. (Tue,) studied this question.