Trapped by Their Own Light: Deployable and Stealth Retroreflective Patch Attacks on Traffic Sign Recognition Systems
This addresses a critical security problem for autonomous vehicle safety by exploiting a stealthy and deployable attack vector, though it is incremental in combining existing attack methods.
The paper tackles the vulnerability of traffic sign recognition systems in autonomous vehicles by introducing the Adversarial Retroreflective Patch (ARP), which achieves a success rate of ≥93.4% in dynamic scenarios at 35 meters and ≥60% against commercial systems in real-world conditions.
Traffic sign recognition plays a critical role in ensuring safe and efficient transportation of autonomous vehicles but remain vulnerable to adversarial attacks using stickers or laser projections. While existing attack vectors demonstrate security concerns, they suffer from visual detectability or implementation constraints, suggesting unexplored vulnerability surfaces in TSR systems. We introduce the Adversarial Retroreflective Patch (ARP), a novel attack vector that combines the high deployability of patch attacks with the stealthiness of laser projections by utilizing retroreflective materials activated only under victim headlight illumination. We develop a retroreflection simulation method and employ black-box optimization to maximize attack effectiveness. ARP achieves $\geq$93.4\% success rate in dynamic scenarios at 35 meters and $\geq$60\% success rate against commercial TSR systems in real-world conditions. Our user study demonstrates that ARP attacks maintain near-identical stealthiness to benign signs while achieving $\geq$1.9\% higher stealthiness scores than previous patch attacks. We propose the DPR Shield defense, employing strategically placed polarized filters, which achieves $\geq$75\% defense success rates for stop signs and speed limit signs against micro-prism patches.