Low‐Interception Waveforms: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples

• Published in: Radio science 2024-08, Vol.59 (8), p.n/a
• Main Authors: Tan, Jia, Xie, Haidong, Zhang, Xiaoying, Ji, Nan, Liao, Haihua, Yu, ZuGuo, Xiang, Xueshuang, Liu, Naijin
• Format: Article
• Language: English
• Subjects: adversarial attack; Algorithms; automatic modulation recognition; Channel noise; Computer vision; Deep learning; Interception; low‐interception waveforms; Modulation; Recognition; Task complexity; Waveforms; Wireless communications
• ISSN: 0048-6604; 1944-799X
• DOI: 10.1029/2022RS007486

Deep learning is applied to many complex tasks in the field of wireless communication, such as modulation recognition of spectrum waveforms, because of its convenience and efficiency. This leads to the problem of a malicious third party using a deep learning model to easily recognize the modulation format of the transmitted waveform. Some existing works address this problem directly using the concept of adversarial examples in the computer vision field without fully considering the characteristics of the waveform transmission in the physical world. Therefore, we propose two low‐interception waveforms (LIWs) generation methods, the LIW and ULIW algorithms, which can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Among them, ULIW improves LIW algorithm by simulating channel noise during training cycle, and substantially reduces the perturbation magnitude while maintaining low interception accuracy. Our LIW and ULIW exhibit significant low‐interception performance in both numerical simulations and hardware experiments. Key Points Define the low‐interception question, and give the mathematical model, application scenarios, and evaluation criteria Propose the ULIW algorithm based on our LIW algorithm, which substantially reduces perturbation while maintaining low interception accuracy Verify the low‐interception performance of LIW and ULIW in both the digital and physical worlds