GLST: Defending Confidence-Driven V2X Collaborative Perception Against Stealthy Multi-Attacker Feature Injection

Abstract

Collaborative perception (CP) improves autonomous-driving perception by enabling connected vehicles to exchange intermediate features via V2X. Confidence-driven sparse communication reduces bandwidth by transmitting only perception-critical spatial regions, but creates a security risk: once a collaborator is compromised, malicious features in high-confidence or ego-uncertain regions may be preferentially selected and amplified during fusion. Using Where2comm as a representative framework, we show that the proposed Pretend Benign attack exploits its spatial-confidence mechanism by injecting stealthy perturbations into uncertain yet perception-critical regions, substantially degrading 3D object detection while preserving benign-like feature characteristics. Beyond this attack-framework pair, we identify a broader weakness of existing trust-based defenses: their reliance primarily on a single consistency signal leaves them vulnerable when multiple attackers form a pseudo-consensus that biases trust estimation. We therefore propose Global-Local Structural Trust (GLST), a lightweight defense that assesses collaborator reliability through three complementary perspectives: global feature consistency, multi-scale local residual consistency, and structural consistency with ego-side semantic topology. The resulting trust scores guide feature fusion to suppress unreliable collaborators. Experiments on OPV2V show that GLST achieves competitive performance against single-attacker Pretend Benign attacks and substantially stronger robustness in multi-attacker settings. Under a four-attacker Pretend Benign attack, GLST maintains 0.69 AP@0.3, whereas existing single-signal defenses degrade severely. GLST also remains effective against gradient-based attacks such as PGD, indicating that multi-level trust modeling is essential for securing confidence-driven CP.