# Among Us: Adversarially Robust Collaborative Perception by Consensus

arXiv: https://arxiv.org/abs/2303.09495 · HTML: https://mlsanity.com/paper/2303.09495/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 50 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 30 of 35)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models](https://mlsanity.com/paper/1712.04248/) (2017)
- [MonoScene: Monocular 3D Semantic Scene Completion](https://mlsanity.com/paper/2112.00726/) (2021)
- [VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion](https://mlsanity.com/paper/2302.12251/) (2023)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://mlsanity.com/paper/2207.02202/) (2022)
- [Learning Distilled Collaboration Graph for Multi-Agent Perception](https://mlsanity.com/paper/2111.00643/) (2021)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022)
- [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019)
- [Understanding and Mitigating the Tradeoff Between Robustness and Accuracy](https://mlsanity.com/paper/2002.10716/) (2020)
- [EPOS: Estimating 6D Pose of Objects with Symmetries](https://mlsanity.com/paper/2004.00605/) (2020)
- [Physically Realizable Adversarial Examples for LiDAR Object Detection](https://mlsanity.com/paper/2004.00543/) (2020)
- [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Improving Black-box Adversarial Attacks with a Transfer-based Prior](https://mlsanity.com/paper/1906.06919/) (2019)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [Adversarial Examples: Opportunities and Challenges](https://mlsanity.com/paper/1809.04790/) (2018)
- [Adversarial Attacks On Multi-Agent Communication](https://mlsanity.com/paper/2101.06560/) (2021)
- [Learning to Communicate and Correct Pose Errors](https://mlsanity.com/paper/2011.05289/) (2020)
- [Uncertainty Quantification of Collaborative Detection for Self-Driving](https://mlsanity.com/paper/2209.08162/) (2022)

## Built on by (most cited first, 30 of 37)

- [An Extensible Framework for Open Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2401.13964/) (2024)
- [SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving](https://mlsanity.com/paper/2306.09001/) (2023)
- [Communication-Efficient Collaborative Perception via Information Filling with Codebook](https://mlsanity.com/paper/2405.04966/) (2024)
- [Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow](https://mlsanity.com/paper/2309.16940/) (2023)
- [STAMP: Scalable Task And Model-agnostic Collaborative Perception](https://mlsanity.com/paper/2501.18616/) (2025)
- [Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception](https://mlsanity.com/paper/2308.16714/) (2023)
- [Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving](https://mlsanity.com/paper/2304.11821/) (2023)
- [Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset](https://mlsanity.com/paper/2406.09383/) (2024)
- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023)
- [A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges](https://mlsanity.com/paper/2404.16139/) (2024)
- [CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perception](https://mlsanity.com/paper/2412.12000/) (2024)
- [CoMamba: Real-time Cooperative Perception Unlocked with State Space Models](https://mlsanity.com/paper/2409.10699/) (2024)
- [GCP: Guarded Collaborative Perception with Spatial-Temporal Aware Malicious Agent Detection](https://mlsanity.com/paper/2501.02450/) (2025)
- [ActFormer: Scalable Collaborative Perception via Active Queries](https://mlsanity.com/paper/2403.04968/) (2024)
- [RCDN: Towards Robust Camera-Insensitivity Collaborative Perception via Dynamic Feature-based 3D Neural Modeling](https://mlsanity.com/paper/2405.16868/) (2024)
- [V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection](https://mlsanity.com/paper/2305.11654/) (2023)
- [Breaking Data Silos: Cross-Domain Learning for Multi-Agent Perception from Independent Private Sources](https://mlsanity.com/paper/2402.04273/) (2024)
- [Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective](https://mlsanity.com/paper/2504.04631/) (2025)
- [NegoCollab: A Common Representation Negotiation Approach for Heterogeneous Collaborative Perception](https://mlsanity.com/paper/2510.27647/) (2025)
- [Module-wise Adaptive Adversarial Training for End-to-end Autonomous Driving](https://mlsanity.com/paper/2409.07321/) (2024)
- [CP-FREEZER: Latency Attacks against Vehicular Cooperative Perception](https://mlsanity.com/paper/2508.01062/) (2025)
- [CoBEVMoE: Heterogeneity-aware Feature Fusion with Dynamic Mixture-of-Experts for Collaborative Perception](https://mlsanity.com/paper/2509.17107/) (2025)
- [Learning Mutual View Information Graph for Adaptive Adversarial Collaborative Perception](https://mlsanity.com/paper/2602.19596/) (2026)
- [On the Federated Learning Framework for Cooperative Perception](https://mlsanity.com/paper/2404.17147/) (2024)
- [SafeCoop: Unravelling Full Stack Safety in Agentic Collaborative Driving](https://mlsanity.com/paper/2510.18123/) (2025)
- [Collaborative Perception in Multi-Robot Systems: Case Studies in Household Cleaning and Warehouse Operations](https://mlsanity.com/paper/2408.14039/) (2024)
- [Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication](https://mlsanity.com/paper/2505.03528/) (2025)
- [CP-uniGuard: A Unified, Probability-Agnostic, and Adaptive Framework for Malicious Agent Detection and Defense in Multi-Agent Embodied Perception Systems](https://mlsanity.com/paper/2506.22890/) (2025)
- [V2X-DSC: Multi-Agent Collaborative Perception with Distributed Source Coding Guided Communication](https://mlsanity.com/paper/2602.00687/) (2026)
- [SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection](https://mlsanity.com/paper/2401.16110/) (2024)

## Abstract (arXiv)

Multiple robots could perceive a scene (e.g., detect objects) collaboratively better than individuals, although easily suffer from adversarial attacks when using deep learning. This could be addressed by the adversarial defense, but its training requires the often-unknown attacking mechanism. Differently, we propose ROBOSAC, a novel sampling-based defense strategy generalizable to unseen attackers. Our key idea is that collaborative perception should lead to consensus rather than dissensus in results compared to individual perception. This leads to our hypothesize-and-verify framework: perception results with and without collaboration from a random subset of teammates are compared until reaching a consensus. In such a framework, more teammates in the sampled subset often entail better perception performance but require longer sampling time to reject potential attackers. Thus, we derive how many sampling trials are needed to ensure the desired size of an attacker-free subset, or equivalently, the maximum size of such a subset that we can successfully sample within a given number of trials. We validate our method on the task of collaborative 3D object detection in autonomous driving scenarios.