PHANTOM: PHysical ANamorphic Threats Obstructing Connected Vehicle Mobility

TL;DR

PHANTOM introduces a novel physical adversarial attack using anamorphic art that deceives connected vehicle perception systems and disrupts V2X communication, revealing significant vulnerabilities in autonomous vehicle safety.

Contribution

The paper presents PHANTOM, a new black-box attack framework using perspective-dependent anamorphic art to deceive multiple object detectors and impair V2X communication in connected autonomous vehicles.

Findings

Achieves over 90% attack success rate in optimal conditions.

Maintains 60-80% effectiveness in degraded environments.

Causes network-wide disruption by propagating false emergency messages.

Abstract

Connected autonomous vehicles (CAVs) rely on vision-based deep neural networks (DNNs) and low-latency (Vehicle-to-Everything) V2X communication to navigate safely and efficiently. Despite their advances, these systems remain vulnerable to physical adversarial attacks. In this paper, we introduce PHANTOM (PHysical ANamorphic Threats Obstructing connected vehicle Mobility), a novel framework for crafting and deploying perspective-dependent adversarial examples using \textit{anamorphic art}. PHANTOM exploits geometric distortions that appear natural to humans but are misclassified with high confidence by state-of-the-art object detectors. Unlike conventional attacks, PHANTOM operates in black-box settings without model access and demonstrates strong transferability across four diverse detector architectures (YOLOv5, SSD, Faster R-CNN, and RetinaNet). Comprehensive evaluation in CARLA across varying speeds, weather conditions, and lighting scenarios shows that PHANTOM achieves over 90\% attack success rate under optimal conditions and maintains 60-80\% effectiveness even in degraded environments. The attack activates within 6-10 meters of the target, providing insufficient time for safe maneuvering. Beyond individual vehicle deception, PHANTOM triggers network-wide disruption in CAV systems: SUMO-OMNeT++ co-simulation demonstrates that false emergency messages propagate through V2X links, increasing Peak Age of Information by 68-89\% and degrading safety-critical communication. These findings expose critical vulnerabilities in both perception and communication layers of CAV ecosystems.