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

TL;DR

This paper introduces a low-interception waveform (LIW) generation method that employs adversarial techniques to prevent unauthorized modulation recognition in wireless communication, maintaining reliable transmission for the intended receiver.

Contribution

The paper presents a novel LIW generation approach tailored for physical wireless transmission, effectively reducing modulation recognition accuracy by adversaries without compromising communication.

Findings

LIW reduces recognition accuracy to about 15%.

Effective in physical hardware experiments.

Maintains reliable communication for friendly parties.

Abstract

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.