标题： 基于自适应元学习的对抗训练用于鲁棒自动调制分类 显示英文标题

标题： Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification

摘要： 基于深度学习的自动调制分类（AMC）模型极易受到对抗攻击的影响，即使是最小的输入扰动也可能导致严重的误分类。 虽然基于对抗攻击对AMC模型进行对抗训练可以显著提高其对该攻击的鲁棒性，但AMC模型在面对其他对抗攻击时仍然毫无防御能力。 对抗扰动理论上存在无限多种可能性，这意味着如果AMC模型被部署到现实世界的通信系统中，它不可避免地会遇到新的未见过的对抗攻击。 此外，实时获取新数据的计算限制和挑战将不允许AMC模型进行全面的训练过程以适应在线时的新攻击。 为此，我们提出了一种基于元学习的对抗训练框架，用于AMC模型，该框架显著增强了对未见过的对抗攻击的鲁棒性，并且仅需少量新的训练样本（如果有可用的话），即可快速适应这些攻击。 我们的结果表明，这种训练框架在比传统AMC模型对抗训练更少的在线训练时间内提供了优越的鲁棒性和准确性，使其非常适合实际部署。 显示英文摘要

摘要： DL-based automatic modulation classification (AMC) models are highly susceptible to adversarial attacks, where even minimal input perturbations can cause severe misclassifications. While adversarially training an AMC model based on an adversarial attack significantly increases its robustness against that attack, the AMC model will still be defenseless against other adversarial attacks. The theoretically infinite possibilities for adversarial perturbations mean that an AMC model will inevitably encounter new unseen adversarial attacks if it is ever to be deployed to a real-world communication system. Moreover, the computational limitations and challenges of obtaining new data in real-time will not allow a full training process for the AMC model to adapt to the new attack when it is online. To this end, we propose a meta-learning-based adversarial training framework for AMC models that substantially enhances robustness against unseen adversarial attacks and enables fast adaptation to these attacks using just a few new training samples, if any are available. Our results demonstrate that this training framework provides superior robustness and accuracy with much less online training time than conventional adversarial training of AMC models, making it highly efficient for real-world deployment.