Communication in multi-agent reinforcement learning (MARL) has been proven to
effectively promote cooperation among agents recently. Since communication in
real-world scenarios is vulnerable to noises and adversarial attacks, it is
crucial to develop robust communicative MARL technique. However, existing
research in this domain has predominantly focused on passive defense
strategies, where agents receive all messages equally, making it hard to
balance performance and robustness. We propose an active defense strategy,
where agents automatically reduce the impact of potentially harmful messages on
the final decision. There are two challenges to implement this strategy, that
are defining unreliable messages and adjusting the unreliable messages' impact
on the final decision properly. To address them, we design an Active Defense
Multi-Agent Communication framework (ADMAC), which estimates the reliability of
received messages and adjusts their impact on the final decision accordingly
with the help of a decomposable decision structure. The superiority of ADMAC
over existing methods is validated by experiments in three
communication-critical tasks under four types of attacks.

多智能体强化学习中的通信经实验证明能有效促进代理之间的合作，然而，由于现实场景中通信容易受到干扰和攻击，因此开发鲁棒通信技术至关重要。本文提出了一种主动防御策略，使代理能够自动减少潜在有害信息对最终决策的影响。我们设计了一个主动防御多智能体通信框架（ADMAC），通过一个可分解的决策结构估计接收到的信息的可靠性，并相应地调整其对最终决策的影响。通过在四种攻击类型下进行三个通信关键任务的实验验证了 ADMAC 相对于现有方法的优越性。

强化学习与主动防御的鲁棒通信多智能体系统

Robust Communicative Multi-Agent Reinforcement Learning with Active  Defense

Object detection has found extensive applications in various tasks, but it is
also susceptible to adversarial patch attacks. Existing defense methods often
necessitate modifications to the target model or result in unacceptable time
overhead. In this paper, we adopt a counterattack approach, following the
principle of "fight fire with fire," and propose a novel and general
methodology for defending adversarial attacks. We utilize an active defense
strategy by injecting two types of defensive patches, canary and woodpecker,
into the input to proactively probe or weaken potential adversarial patches
without altering the target model. Moreover, inspired by randomization
techniques employed in software security, we employ randomized canary and
woodpecker injection patterns to defend against defense-aware attacks. The
effectiveness and practicality of the proposed method are demonstrated through
comprehensive experiments. The results illustrate that canary and woodpecker
achieve high performance, even when confronted with unknown attack methods,
while incurring limited time overhead. Furthermore, our method also exhibits
sufficient robustness against defense-aware attacks, as evidenced by adaptive
attack experiments.

本文提出了一种新颖且通用的对抗攻击防御方法，采用了 “以火攻火” 的原则，并通过注入两种类型的防御贴片，Canary 和 Woodpecker，实施主动防御策略，以主动检测和削弱潜在的对抗贴片，而无需修改目标模型。同时，采用软件安全中的随机化技术，使用随机注入方式来抵御能够识别防御的攻击。通过全面的实验证明了所提出的方法的有效性和实用性，结果表明 Canary 和 Woodpecker 在面对未知攻击方法时也能取得很高的性能，且对时间开销的影响有限。此外，通过适应性攻击实验，也显示出本方法对于防御识别攻击具有足够的鲁棒性。