Author

Ming F. Li

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Preprint Oct 2026

Don't Let One Lie Survive A Hundred Truths: A Selective Bayesian Trust Estimator for Collaborative Perception

Collaborative perception (CP) enables connected vehicles to see beyond their own sensors but makes them dependent on messages they cannot independently verify. A compromised collaborator can surgically conceal a single safety-critical object or inject a non-existing one while correctly reporting many others. Existing B...

Yu-Tong Liu, Chen-Yi Wang, Ming F. Li et al. · 0 citations

Preprint Sep 2026

Investigating Adversarial Robustness of Heterogeneous Cooperative Perception

Heterogeneous cooperative perception (CP) enables connected vehicles with diverse sensor setups to share spatial awareness via compact feature maps, where receivers reconcile these maps using learned translation modules for fusion and inference. Prior attacks against CP in a homogeneous setting reveal that the data exc...

Chen-Yi Wang, Yu-Tong Liu, Qing-Zhao Zhang et al. · 0 citations

#artificial intelligence May 2026

Adversarial Trust Poisoning in Vehicular Collaborative Perception

This work presents TrustFlip, a novel attack that weaponizes consistency-based defenses to poison the trust assigned to benign vehicles, and introduces TrustReflect, a lightweight self-reflection mechanism that marks disputed regions as uncertain and excludes them from trust evaluation, reducing the attack success rate...

Yu-Tong Liu, Chenyi Wang, Ming F. Li et al. · arXiv.org · 0 citations