Trapped by Their Own Light: Deployable and Stealth Retroreflective Patch Attacks on Traffic Sign Recognition Systems
Go Tsuruoka, Takami Sato, Qi Alfred Chen, Kazuki Nomoto, Ryunosuke Kobayashi, Yuna Tanaka, Tatsuya Mori

TL;DR
This paper introduces the Adversarial Retroreflective Patch (ARP), a stealthy and deployable attack on traffic sign recognition systems that uses retroreflective materials activated by vehicle headlights, achieving high success rates and maintaining stealth.
Contribution
The paper presents ARP, a novel retroreflective patch attack combining deployability and stealth, along with a simulation method and a defense strategy using polarized filters.
Findings
ARP achieves ≥93.4% success rate at 35 meters in dynamic scenarios.
ARP maintains near-identical stealthiness to benign signs.
DPR Shield defense achieves ≥75% success against micro-prism patches.
Abstract
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 93.4\% success rate in dynamic scenarios at 35 meters and 60\% success rate against commercial TSR…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Neural Network Applications · Advanced Optical Sensing Technologies
