Deep learning (DL) has been widely studied for assisting applications of modern wireless communications. One of the applications is automatic modulation classification (AMC). However, DL models are found to be vulnerable to adversarial machine learning (AML) threats. One of the most persistent and stealthy threats is the backdoor (Trojan) attack. Nevertheless, most studied threats originate from other AI domains, such as computer vision (CV). Therefore, in this paper, a physical backdoor attack targeting the wireless signal before transmission is studied. The adversary is considered to be using explainable AI (XAI) to guide the placement of the trigger in the most vulnerable parts of the signal. Then, a class prototype combined with principal components is used to generate the trigger. The studied threat was found to be efficient in breaching multiple DL-based AMC models. The attack achieves high success rates for a wide range of SNR values and a small poisoning ratio.

翻译：深度学习（DL）已被广泛研究以辅助现代无线通信应用，其中自动调制分类（AMC）是重要应用之一。然而，研究发现深度学习模型易受对抗性机器学习（AML）威胁，其中持续性和隐蔽性最强的威胁之一便是后门（木马）攻击。但现有研究中的威胁多源自其他人工智能领域（如计算机视觉CV）。因此，本文研究了一种针对无线信号在传输前物理实施的后门攻击。假设攻击者利用可解释人工智能（XAI）引导触发器放置在信号最脆弱的部分，进而结合主成分的类原型生成触发器。研究发现，该威胁能有效突破多种基于深度学习的自动调制分类模型，在广泛信噪比（SNR）范围及较低投毒比例下均能达到高成功率。