Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples
This addresses security concerns in wireless communication by preventing unauthorized modulation recognition, though it is incremental as it builds on adversarial example concepts applied to a specific domain.
The paper tackles the problem of malicious third parties using deep learning to recognize modulation formats of transmitted waveforms, proposing a low-intercept waveform generation method that reduces recognition probability to about 15% accuracy with small perturbations, without affecting reliable communication for friendly parties.
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 image domain without fully considering the characteristics of the waveform transmission in the physical world. Therefore, we propose a low-intercept waveform~(LIW) generation method that can reduce the probability of the modulation being recognized by a third party without affecting the reliable communication of the friendly party. Our LIW exhibits significant low-interception performance even in the physical hardware experiment, decreasing the accuracy of the state of the art model to approximately $15\%$ with small perturbations.