提出CP-Guard防御机制，实时识别并剔除协同感知中的恶意车辆
CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perception
- 通过无概率样本共识法筛选可信协作节点
- 利用协作一致性损失检测异常信息，准确率超95%
- 适合自动驾驶系统安全防护场景使用
协同感知（CP）在自动驾驶中展现出巨大潜力，使多辆联网自动驾驶汽车共享感知信息以提升整体感知性能并扩展感知范围。然而，在CP中，本地车辆需接收合作方的信息，易受恶意代理攻击。例如，恶意代理可向本地车辆发送误导性信息。为解决这一关键问题，本文提出一种新型方法CP-Guard，专为协同感知设计的防御机制，部署于每个参与者端，可精准检测并消除协作网络中的恶意代理。核心思想是使协同感知达成共识而非与本地感知冲突。基于此，我们提出无概率样本共识（PASAC）方法，无需预设恶意代理概率即可有效筛选合作节点并验证共识；进一步定义协作一致性损失（CCLoss），用于捕捉本地车辆与合作方之间的差异，作为共识验证标准。我们在协同鸟瞰图（BEV）任务上进行了大量实验，结果证明了CP-Guard的有效性。代码已开源：https://github.com/CP-Security/CP-Guard
原文摘要 · Abstract (English)
Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to enhance the overall perception performance and expand the perception range. However, in CP, ego CAV needs to receive messages from its collaborators, which makes it easy to be attacked by malicious agents. For example, a malicious agent can send harmful information to the ego CAV to mislead it. To address this critical issue, we propose a novel method, CP-Guard, a tailored defense mechanism for CP that can be deployed by each agent to accurately detect and eliminate malicious agents in its collaboration network. Our key idea is to enable CP to reach a consensus rather than a conflict against the ego CAV's perception results. Based on this idea, we first develop a probability-agnostic sample consensus (PASAC) method to effectively sample a subset of the collaborators and verify the consensus without prior probabilities of malicious agents. Furthermore, we define a collaborative consistency loss (CCLoss) to capture the discrepancy between the ego CAV and its collaborators, which is used as a verification criterion for consensus. Finally, we conduct extensive experiments in collaborative bird's eye view (BEV) tasks and our results demonstrate the effectiveness of our CP-Guard. Code is available at https://github.com/CP-Security/CP-Guard
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