Wavelet Domain Frequency Steganography Backdoor Attack for Misleading Automatic Modulation Classification
Sicheng Zhang, Longfei Li, Zixin Li, Haichao Zhang, Guangzhen Si, Yu Wang, Guan Gui, Yun Lin
Abstract
Deep learning (DL)-based automatic modulation classification (AMC) is increasingly utilized in wireless applications, particularly within the Internet of Things (IoT) ecosystem. However, the open data collection for these systems can lead to vulnerabilities, as the data sets are susceptible to malicious manipulations, potentially resulting in backdoor attacks. In this article, we propose a novel wavelet domain frequency steganography (WDFS) backdoor attack method to demonstrate this security flaw, designed explicitly for misleading AMC. This method employs discrete wavelet transform and singular value decomposition to segment signals into distinct wavelet domain frequency components. We embed the backdoor trigger directly into these components, ensuring it is sample-specific and undetectable. Extensive testing shows that our WDFS method outperforms existing methods in terms of attack efficiency and stealth and successfully evades several advanced backdoor defense mechanisms, demonstrating its robustness. These findings highlight the urgent need for enhanced security measures in AMC systems within the artificial intelligence domain.