提出新方法检测协作感知中的恶意车辆，提升自动驾驶安全性
CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in Collaborative Perception
- 在特征层面识别恶意车辆，无需验证最终感知结果，降低计算开销
- 构建首个专用数据集CP-GuardBench，支持恶意检测模型训练与评估
- 设计双中心对比损失，增强良性和恶意特征的区分度，适合车联网安全研究者
协作感知（CP）是实现智能网联汽车安全驾驶的有前景技术，通过多车共享感知信息提升感知性能。然而，相较于单车感知，CP系统的开放性使其更易遭受恶意攻击，攻击者可注入虚假信息误导本车感知，带来严重安全隐患。为此，本文首次提出一种新型恶意代理检测范式，可在特征层面有效识别恶意代理，无需依赖最终感知结果的验证，显著降低计算开销。基于该范式，我们构建了首个综合性数据集CP-GuardBench，用于训练和评估各类恶意代理检测方法。进一步地，提出鲁棒防御方法CP-Guard+，通过精心设计的双中心对比损失（DCCLoss），增强良性与恶意特征表示之间的区分边界。在CP-GuardBench与V2X-Sim两个数据集上进行大量实验，充分验证了CP-Guard+的有效性与优越性。
原文摘要 · Abstract (English)
Collaborative perception (CP) is a promising method for safe connected and autonomous driving, which enables multiple vehicles to share sensing information to enhance perception performance. However, compared with single-vehicle perception, the openness of a CP system makes it more vulnerable to malicious attacks that can inject malicious information to mislead the perception of an ego vehicle, resulting in severe risks for safe driving. To mitigate such vulnerability, we first propose a new paradigm for malicious agent detection that effectively identifies malicious agents at the feature level without requiring verification of final perception results, significantly reducing computational overhead. Building on this paradigm, we introduce CP-GuardBench, the first comprehensive dataset provided to train and evaluate various malicious agent detection methods for CP systems. Furthermore, we develop a robust defense method called CP-Guard+, which enhances the margin between the representations of benign and malicious features through a carefully designed Dual-Centered Contrastive Loss (DCCLoss). Finally, we conduct extensive experiments on both CP-GuardBench and V2X-Sim, and demonstrate the superiority of CP-Guard+.
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