Investigating Physical Latency Attacks against Camera-based Perception

Camera-based perception is a central component to the visual perception of autonomous systems. Recent works have investigated latency attacks against perception pipelines, which can lead to a Denial-of-Service against the autonomous system. Unfortunately, these attacks lack real-world applicability, either relying on digital perturbations or requiring large, unscalable, and highly visible patches that cover up the victim’s view. In this paper, we propose DetStorm, a novel physically realizable latency attack against camera-based perception. DetStorm uses projector perturbations to cause delays in perception by creating a large number of adversarial objects. These objects are optimized on four objectives to evade filtering by multiple Non-Maximum Suppression (NMS) approaches. To maximize the number of created objects in a dynamic physical environment, DetStorm takes a unique greedy approach, segmenting the environment into "zones" containing distinct object classes and maximizing the number of created objects per zone. DetStorm adapts to changes in the environment in real time, recombining perturbation patterns via our zone stitching process into a contiguous, physically projectable image. Evaluations in both simulated and real-world experiments show that DetStorm causes a 506% increase in detected objects on average, delaying perception results by up to 8.1 seconds, and capable of causing physical consequences on real-world autonomous driving systems.