mlsanityData to 14 Oct 2026

CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perception

arXiv 2412.12000 ↗First posted 16 Dec 2024cs.AIcited by 13

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Abstract (arXiv)

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

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 30 of its 53 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

30 of 53 references.

References · 30

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  4. 2020V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Predictioncited by 292
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  6. 2020Understanding and Mitigating the Tradeoff Between Robustness and Accuracycited by 149
  7. 2020Physically Realizable Adversarial Examples for LiDAR Object Detectioncited by 137
  8. 2022Robust Collaborative 3D Object Detection in Presence of Pose Errorscited by 129
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  10. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  11. 2023Collaborative Perception in Autonomous Driving: Methods, Datasets and Challengescited by 87
  12. 2024An Extensible Framework for Open Heterogeneous Collaborative Perceptioncited by 87
18 more
  1. 2023Split Learning in 6G Edge Networkscited by 68
  2. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  3. 2021Adversarial Attacks On Multi-Agent Communicationcited by 49
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  5. 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
  6. 2023Among Us: Adversarially Robust Collaborative Perception by Consensuscited by 37
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  9. 2021Fooling LiDAR Perception via Adversarial Trajectory Perturbationcited by 25
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  11. 2024AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Mergingcited by 22
  12. 2023Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Drivingcited by 19
  13. 2024Directed-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attentioncited by 14
  14. 2024R-ACP: Real-Time Adaptive Collaborative Perception Leveraging Robust Task-Oriented Communicationscited by 13
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  16. 2024What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perceptioncited by 11
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  18. 2024PIB: Prioritized Information Bottleneck Framework for Collaborative Edge Video Analyticscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 7.

13 papers.

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  1. 2025GCP: Guarded Collaborative Perception with Spatial-Temporal Aware Malicious Agent Detectioncited by 8
  2. 2025Distribution-Aligned Decoding for Efficient LLM Task Adaptationcited by 7
  3. 2025InfoCom: Kilobyte-Scale Communication-Efficient Collaborative Perception with Information Bottleneckcited by 3
  4. 2026Learning Mutual View Information Graph for Adaptive Adversarial Collaborative Perceptioncited by 3
  5. 2025Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edgecited by 2
  6. 2026RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Facescited by 2
  7. 2025CP-uniGuard: A Unified, Probability-Agnostic, and Adaptive Framework for Malicious Agent Detection and Defense in Multi-Agent Embodied Perception Systemscited by 1
  8. 2026Decoder Gradient Shields: A Family of Provable and High-Fidelity Methods Against Gradient-Based Box-Free Watermark Removalcited by 1
  9. 2026V2X-DSC: Multi-Agent Collaborative Perception with Distributed Source Coding Guided Communicationcited by 1
  10. 2026V2VCrafter: Consistent Street-View Image Generation Across Vehiclescited by 1
  11. 2025Privacy-Aware Sharing of Raw Spatial Sensor Data for Cooperative Perceptioncited by 0
  12. 2026All Vehicles Can Lie: Efficient Adversarial Defense in Fully Untrusted-Vehicle Collaborative Perception via Pseudo-Random Bayesian Inferencecited by 0
1 more
  1. 2026HyDRA: Hybrid Domain-Aware Robust Architecture for Heterogeneous Collaborative Perceptioncited by 0