Physical Backdoor Attack Against Deep Learning-Based Modulation Classification

Computer Science > Cryptography and Security [Submitted on 26 Mar 2026] Physical Backdoor Attack Against Deep Learning-Based Modulation Classification Younes Salmi, Hanna Bogucka Deep Learning (DL) has become a key technology that assists radio frequency (RF) signal classification applications, such as modulation classification. However, the DL models are vulnerable to adversarial machine learning threats, such as data manipulation attacks. We study a physical backdoor (Trojan) attack that targets a DL-based modulation classifier. In contrast to digital backdoor attacks, where digital triggers are injected into the training dataset, we use power amplifier (PA) non-linear distortions to create physical triggers before the dataset is formed. During training, the adversary manipulates amplitudes of RF signals and changes their labels to a target modulation scheme, training a backdoored model. At inference, the adversary aims to keep the backdoor attack inactive such that the backdoored model maintains high accuracy on test signals. However, if they apply the same manipulation used during training on these test signals, the backdoor is activated, and the model misclassifies these signals. We demonstrate that our proposed attack achieves high attack success rates with few manipulated RD signals for different noise levels. Furthermore, we test the resilience of the proposed attack to multiple defense techniques, and the results show that these techniques fail to mitigate the attack. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.25304 [cs.CR]   (or arXiv:2603.25304v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.25304 Focus to learn more Related DOI: https://doi.org/10.1109/MeditCom64437.2025.11104329 Focus to learn more Submission history From: Hanna Bogucka [view email] [v1] Thu, 26 Mar 2026 10:49:07 UTC (165 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?)