低截获波形：通过对抗性示例防止频谱波形调制的识别
Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples
摘要：
深度学习因其方便、高效的特点，被应用于无线通信领域的许多复杂任务，如频谱波形的调制识别等。这导致了恶意第三方使用深度学习模型来轻松识别传输波形的调制格式的问题。现有的一些工作解决这个问题，直接使用的概念，对抗的例子在图像域中没有充分考虑的波形在物理世界中的传输特性。因此，本文提出了一种低截获波形LIW生成方法，在不影响友方可靠通信的前提下，降低了调制被第三方识别的概率。即使在物理硬件实验中，我们的LIW也表现出显着的低拦截性能，将最先进模型的精度降低到约$15\%$，具有小扰动。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
