arXiv 2501.024502025-01cs.CV

GCP: 带空间-时间感知恶意代理检测的防护协作感知

GCP: Guarded Collaborative Perception with Spatial-Temporal Aware Malicious Agent Detection

Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Department of Robotics, University of Michigan（密歇根大学机器人系）

Yihang Tao, Senkang Hu, Yue Hu, Haonan An, Hangcheng Cao, Yuguang Fang

备注：: Accepted by IEEE TDSC

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AI总结：

本文提出GCP框架，通过空间-时间感知恶意代理检测提升协作感知安全性，采用置信度加权空间一致性损失和联合空间-时间Benjamini-Hochberg检验，有效应对恶意代理攻击，实验显示在BAC攻击下AP@0.5提升34.69%。

摘要：

协作感知通过连接和自动驾驶车辆之间的信息共享显著提高了自动驾驶安全性。然而，恶意代理的对抗性信息攻击会导致性能严重退化。现有防御方法基于单次异常检测框架，但忽视了时间信息相关性，易被模型输入输出空间中的细微扰动所绕过。本文揭示了一种新型盲区混淆（BAC）攻击，该攻击能破坏现有基于单次异常检测的方法。为应对此问题，我们提出GCP框架，基于空间-时间感知恶意代理检测，通过置信度加权空间一致性损失保持单次空间一致性，并通过重建低置信度区域的历史鸟瞰图运动流来检测时间异常。我们还采用联合空间-时间Benjamini-Hochberg检验来合成双域异常结果以实现可靠的恶意代理检测。大量实验表明，GCP在各种攻击场景下表现出色，相较于现有最佳CP防御策略，在BAC攻击下AP@0.5提升高达34.69%，同时在其他典型攻击下保持5-8%的改进。代码将在https://github.com/yihangtao/GCP.git发布。

英文摘要：

Collaborative perception significantly enhances autonomous driving safety by extending each vehicle's perception range through message sharing among connected and autonomous vehicles. Unfortunately, it is also vulnerable to adversarial message attacks from malicious agents, resulting in severe performance degradation. While existing defenses employ hypothesis-and-verification frameworks to detect malicious agents based on single-shot outliers, they overlook temporal message correlations, which can be circumvented by subtle yet harmful perturbations in model input and output spaces. This paper reveals a novel blind area confusion (BAC) attack that compromises existing single-shot outlier-based detection methods. As a countermeasure, we propose GCP, a Guarded Collaborative Perception framework based on spatial-temporal aware malicious agent detection, which maintains single-shot spatial consistency through a confidence-scaled spatial concordance loss, while simultaneously examining temporal anomalies by reconstructing historical bird's eye view motion flows in low-confidence regions. We also employ a joint spatial-temporal Benjamini-Hochberg test to synthesize dual-domain anomaly results for reliable malicious agent detection. Extensive experiments demonstrate GCP's superior performance under diverse attack scenarios, achieving up to 34.69% improvements in AP@0.5 compared to the state-of-the-art CP defense strategies under BAC attacks, while maintaining consistent 5-8% improvements under other typical attacks. Code will be released at https://github.com/yihangtao/GCP.git.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。