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arXiv AI By Yutong Liu, Chenyi Wang, Ming F. Li, Qingzhao Zhang

Adversarial Trust Poisoning in Vehicular Collaborative Perception

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The paper introduces TrustFlip, an attack that exploits consistency‑based defenses in vehicular collaborative perception by deploying physical adversarial objects to create inconsistent observations among benign vehicles. This misattribution lowers the trust score of a targeted vehicle, leading to its exclusion from the collaboration and a degradation of perception performance. The authors evaluate the attack across multiple architectures, showing it can remove a benign vehicle in up to 87.7% of scenarios and reduce Average Precision by up to 13%, and propose a mitigation called TrustReflect that reduces the attack success rate by 35–100%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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