Weaponizing Reflectivity for Pointcloud Deception with Forged Invisible Geometries

Pointcloud perception systems are critical for autonomous driving, robotics, and embodied intelligence. While existing adversarial attack research predominantly focuses on digital manipulations, this paper introduces a novel physicalworld attack, Stride, that strategically disrupts pointcloud classification, segmentation, and object detection by exploiting the walk error, a systematic depth measurement error induced by reflectivity variations. By developing a quantitative model that maps reflectivity variations to depth errors, we demonstrate how infrared-specific dyes, inconspicuous in the visible light spectrum, can systematically manipulate pointcloud representations, fundamentally compromising the reliability of perception algorithms. To this end, our approach presents a physically feasible optimization method for applying inconspicuous coatings that can induce targeted misclassifications, missegmentations, and object detection failures without resorting to digital manipulations. Experiments across LiDAR and depth cameras reveal significant performance degradation in downstream perception tasks, highlighting the profound vulnerabilities of current 3D sensing technologies. These findings underscore the critical need for robust sensing mechanisms capable of withstanding subtle yet strategic physical interventions.