Robust Collaborative Perception: Combining Adversarial Training with Consensus Mechanism for Enhanced V2X Security

Collaborative perception enhances the robustness and accuracy of autonomous systems by leveraging shared perceptual data across agents, particularly through feature-level fusion, which balances communication efficiency with contextual preservation. However, this data-sharing introduces vulnerabilities, as adversaries can inject malicious perturbations, compromising system reliability in safety-critical scenarios. In this work, we address the adversarial robustness of feature-level fusion in collaborative perception under white-box untargeted attack settings. We propose a novel framework that combines adversarial training with a consensus mechanism, enhancing resilience to adversarial perturbations in a model-agnostic manner. Our approach not only improves robustness against attacks but also enhances performance on clean data, achieving at least 5% improvement in average precision. Extensive experiments on the V2XSet dataset with four adversarial attack types and two collaborative perception methods demonstrate the effectiveness of our method, outperforming consensus defense and adversarial training alone consistently under different adversarial perturbation magnitudes. These findings underscore the potential of our approach to advance secure and reliable collaborative perception systems.