Abstract
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 Bayesian trust mechanisms pool agreement across objects, which, while effective against blatant untargeted attacks, either incurs high false-positive rates (FPR), or allows unrelated correct reports to dilute persistent attack evidence for stealthy single-object attackers. To address this problem, we propose SABER, a selective two-tier Bayesian trust estimator. The first tier maintains broad agent and object trust, preserving the ability to downweight benign but low-quality contributors. Cumulative-sum screening selects agent--object pairs with persistent omissions or unsupported reports for focused Bayesian assessment. The second tier checks these pairs against other agents' evidence and maintains a separate, reference-weighted Beta state for each. The lowest pair score constrains agent trust, preventing unrelated reports from diluting a targeted attack. We establish sufficient conditions for stronger attacker-side trust reductions with bounded additional benign false alarms at fixed thresholds. Compared with state-of-the-art CP defenses, SABER improves attack detection while reducing benign FPRs. On OPV2V, SABER improves defense ROC-AUC over MATE by up to 0.427 in late fusion and 0.337 in intermediate fusion. Against advanced intermediate-fusion data fabrication attacks, it increases detection rates over ROBOSAC and LUCIA by up to 96.40 and 67.07 percentage points, respectively, while reducing FPRs.
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Cite this article
APA 7
Liu, Y., Wang, C., Li, M. F., & Zhang, Q. (2026). Don't Let One Lie Survive A Hundred Truths: A Selective Bayesian Trust Estimator for Collaborative Perception. https://omanscience.com/en/articles/don-t-let-one-lie-survive-a-hundred-truths-a-selective-bayesian-trust-estimator-for-collaborative-perception
MLA 9
Liu, Yutong, et al. "Don't Let One Lie Survive A Hundred Truths: A Selective Bayesian Trust Estimator for Collaborative Perception." https://omanscience.com/en/articles/don-t-let-one-lie-survive-a-hundred-truths-a-selective-bayesian-trust-estimator-for-collaborative-perception.
Chicago (author–date)
Liu, Yutong, Chenyi Wang, Ming F. Li, and Qingzhao Zhang. 2026. "Don't Let One Lie Survive A Hundred Truths: A Selective Bayesian Trust Estimator for Collaborative Perception." https://omanscience.com/en/articles/don-t-let-one-lie-survive-a-hundred-truths-a-selective-bayesian-trust-estimator-for-collaborative-perception.
Harvard
Liu, Y., Wang, C., Li, M. F. and Zhang, Q. (2026) 'Don't Let One Lie Survive A Hundred Truths: A Selective Bayesian Trust Estimator for Collaborative Perception', Available at: https://omanscience.com/en/articles/don-t-let-one-lie-survive-a-hundred-truths-a-selective-bayesian-trust-estimator-for-collaborative-perception.
Vancouver
Liu Y, Wang C, Li MF, Zhang Q. Don't Let One Lie Survive A Hundred Truths: A Selective Bayesian Trust Estimator for Collaborative Perception. https://omanscience.com/en/articles/don-t-let-one-lie-survive-a-hundred-truths-a-selective-bayesian-trust-estimator-for-collaborative-perception
IEEE
Y. Liu, C. Wang, M. F. Li, and Q. Zhang, "Don't Let One Lie Survive A Hundred Truths: A Selective Bayesian Trust Estimator for Collaborative Perception," https://omanscience.com/en/articles/don-t-let-one-lie-survive-a-hundred-truths-a-selective-bayesian-trust-estimator-for-collaborative-perception.