Despite significant advancements in active learning and adversarial attacks, the intersection of these two fields remains underexplored, particularly in developing robust active learning frameworks against dynamic adversarial threats. The challenge of developing robust active learning frameworks under dynamic adversarial attacks is critical, as these attacks can lead to catastrophic forgetting within the active learning cycle. This paper introduces Robust Active Learning (RoAL), a novel approach designed to address this issue by integrating Elastic Weight Consolidation (EWC) into the active learning process. Our contributions are threefold: First, we propose a new dynamic adversarial attack that poses significant threats to active learning frameworks. Second, we introduce a novel method that combines EWC with active learning to mitigate catastrophic forgetting caused by dynamic adversarial attacks. Finally, we conduct extensive experimental evaluations to demonstrate the efficacy of our approach. The results show that RoAL not only effectively counters dynamic adversarial threats but also significantly reduces the impact of catastrophic forgetting, thereby enhancing the robustness and performance of active learning systems in adversarial environments.

翻译：尽管主动学习与对抗攻击领域已取得显著进展，但这两个领域的交叉研究仍显不足，尤其是在开发能够抵御动态对抗威胁的鲁棒主动学习框架方面。在动态对抗攻击下构建鲁棒主动学习框架至关重要，因为此类攻击可能导致主动学习循环中出现灾难性遗忘。本文提出鲁棒主动学习（RoAL），这是一种通过将弹性权重巩固（EWC）整合到主动学习流程中以解决该问题的新方法。我们的贡献包含三个方面：首先，我们提出一种对主动学习框架构成显著威胁的新型动态对抗攻击方法。其次，我们引入一种将EWC与主动学习相结合的新方法，以减轻动态对抗攻击引发的灾难性遗忘。最后，我们通过大量实验评估验证了所提方法的有效性。结果表明，RoAL不仅能有效抵御动态对抗威胁，还能显著降低灾难性遗忘的影响，从而提升对抗环境下主动学习系统的鲁棒性与性能。