Among Us：通过共识实现对抗鲁棒的协同感知
Among Us: Adversarially Robust Collaborative Perception by Consensus
AI总结：
针对多机器人协同感知易受未知对抗攻击的问题，提出基于采样与共识验证的 ROBOSAC 防御框架，并给出采样试验次数的理论保证。
摘要：
多个机器人可以协同感知场景（例如检测物体），其效果优于个体，但在使用深度学习时容易遭受对抗攻击。这可以通过对抗防御来解决，但其训练通常需要往往未知的攻击机制。与之不同，我们提出 ROBOSAC，这是一种新颖的基于采样的防御策略，可泛化到未见过的攻击者。我们的核心思想是，与个体感知相比，协同感知在结果上应带来共识而非分歧。这引出了我们的假设并验证框架：比较来自随机队友子集在有协同与无协同时的感知结果，直到达成共识。在该框架中，被采样子集中有更多队友通常意味着更好的感知性能，但也需要更长的采样时间来拒绝潜在攻击者。因此，我们推导了需要多少次采样试验，才能确保无攻击者子集达到期望规模；或者等价地，在给定试验次数内能够成功采样出的此类子集的最大规模。我们在自动驾驶场景中的协同3D目标检测任务上验证了该方法。
英文摘要：
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.
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。
