Deep learning based automatic modulation classification (AMC) has received significant attention owing to its potential applications in both military and civilian use cases. Recently, data-driven subsampling techniques have been utilized to overcome the challenges associated with computational complexity and training time for AMC. Beyond these direct advantages of data-driven subsampling, these methods also have regularizing properties that may improve the adversarial robustness of the modulation classifier. In this paper, we investigate the effects of an adversarial attack on an AMC system that employs deep learning models both for AMC and for subsampling. Our analysis shows that subsampling itself is an effective deterrent to adversarial attacks. We also uncover the most efficient subsampling strategy when an adversarial attack on both the classifier and the subsampler is anticipated.

翻译：基于深度学习的自动调制识别（AMC）因其在军事和民用场景中的潜在应用而备受关注。近年来，数据驱动子采样技术被用于应对AMC中计算复杂度和训练时间的挑战。除这些直接优势外，此类方法还具有正则化特性，可提升调制分类器对抗鲁棒性。本文研究了针对采用深度学习模型进行AMC和子采样的系统的对抗攻击影响。我们的分析表明，子采样本身是对抗攻击的有效威慑手段。同时，我们揭示了在预期分类器和子采样器均遭受对抗攻击时最高效的子采样策略。