Cooperative perception (CP) enhances situational awareness of connected and autonomous vehicles by exchanging and combining messages from multiple agents. While prior work has explored adversarial integrity attacks that degrade detection accuracy, little is known about CP's robustness against attacks on timeliness (or availability), a safety-critical requirement for autonomous driving. In this paper, we present CP-FREEZER, the first latency attack that maximizes the computation delay of CP algorithms by injecting adversarial perturbation via V2V messages. Our attack resolves several unique challenges, including the non-differentiability of point cloud preprocessing, asynchronous knowledge of the victim’s input due to transmission delays, and uses a novel loss function that effectively maximizes the execution time of the CP pipeline. Extensive experiments show that CP-FREEZER increases end-to-end CP latency by over 90×, pushing per-frame processing time beyond 3 seconds with a 100% success rate on our real-world vehicle testbed. Our findings reveal a critical threat to the availability of CP systems, highlighting the urgent need for robust defenses.

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.

Multi-Object Tracking (MOT) is a critical task in computer vision, with applications ranging from surveillance systems to autonomous driving. However, threats to MOT algorithms have yet been widely studied. In particular, incorrect association between the tracked objects and their assigned IDs can lead to severe consequences, such as wrong trajectory predictions. Previous attacks against MOT either focused on hijacking the trackers of individual objects, or manipulating the tracker IDs in MOT by attacking the integrated object detection (OD) module in the digital domain, which are model-specific, non-robust, and only able to affect specific samples in offline datasets. In this paper, we present AdvTraj, the first online and physical ID-manipulation attack against tracking-by-detection MOT, in which an attacker uses adversarial trajectories to transfer its ID to a targeted object to confuse the tracking system, without attacking OD. Our simulation results in CARLA show that AdvTraj can fool ID assignments with 100% success rate in various scenarios for white-box attacks against SORT, which also have high attack transferability (up to 93% attack success rate) against state-of-the-art (SOTA) MOT algorithms due to their common design principles. We characterize the patterns of trajectories generated by AdvTraj and propose two universal adversarial maneuvers that can be performed by a human walker/driver in daily scenarios. Our work reveals under-explored weaknesses in the object association phase of SOTA MOT systems, and provides insights into enhancing the robustness of such systems.

Modern autonomous systems rely on both object detection and object tracking in their visual perception pipelines. Although many recent works have attacked the object detection component of autonomous vehicles, these attacks do not work on full pipelines that integrate object tracking to enhance the object detector's accuracy. Meanwhile, existing attacks against object tracking either lack real-world applicability or do not work against a powerful class of object trackers, Siamese trackers. In this paper, we present AttrackZone, a new physically-realizable tracker hijacking attack against Siamese trackers that systematically determines valid regions in an environment that can be used for physical perturbations. AttrackZone exploits the heatmap generation process of Siamese Region Proposal Networks in order to take control of an object's bounding box, resulting in physical consequences including vehicle collisions and masked intrusion of pedestrians into unauthorized areas. Evaluations in both the digital and physical domain show that AttrackZone achieves its attack goals 92% of the time, requiring only 0.3-3 seconds on average.