DSADA: Detecting Spoofing Attacks in Driver Assistance Systems Using Objects’ Spatial Shapes

Object detection algorithms suffer from a perceptual vulnerability where they cannot differentiate between counterfeit and real objects. In this paper, we investigate the perceptual vulnerability in advanced driver assistance systems (ADAS) when faced with physical and digital spoofing attacks. To address this vulnerability, we propose a method named DSADA (Detecting Spoofing Attacks in Driver Assistance) to mitigate creation and misclassification spoofing attacks against object detection algorithms utilizing the LiDAR point clouds and objects’ spatial shapes. DSADA receives the outcomes of the object detection algorithm along with the corresponding LiDAR point clouds for each scene. DSADA exploits the spatial shapes of objects obtained from the point clouds to cross-validate the outcomes of the object detection algorithm. Any discrepancy results in generating an alert to warn about the spoofing attack. We analyze defense-aware and unaware attacks against DSADA. The evaluation results show the effectiveness of the suggested method with a true positive rate of 100% and a low false positive rate of only 3.97%. The comparative evaluation validates that the suggested method identifies a broader range of spoofed objects, including projected, displayed and printed ones, while narrowing the scope of potential attacks to familiar objects in the driving context.