On the Vulnerability of Deep Automatic Modulation Classifiers to Explainable Backdoor Threats

Computer Science > Cryptography and Security [Submitted on 26 Mar 2026] On the Vulnerability of Deep Automatic Modulation Classifiers to Explainable Backdoor Threats Younes Salmi, Hanna Bogucka 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. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.25310 [cs.CR]   (or arXiv:2603.25310v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.25310 Focus to learn more Related DOI: https://doi.org/10.1109/CCNC65079.2026.11366381 Focus to learn more Submission history From: Hanna Bogucka [view email] [v1] Thu, 26 Mar 2026 10:57:00 UTC (111 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)