Recently, some researches are devoted to the topic of end-to-end learning a physical layer secure communication system based on autoencoder under Gaussian wiretap channel. However, in those works, the reliability and security of the encoder model were learned through necessary decoding outputs of not only legitimate receiver but also the eavesdropper. In fact, the assumption of known eavesdropper's decoder or its output is not practical. To address this issue, in this paper we propose a dual mutual information neural estimation (MINE) based neural secure communications model. The security constraints of this method is constructed only with the input and output signal samples of the legal and eavesdropper channels and benefit that training the encoder is completely independent of the decoder. Moreover, since the design of secure coding does not rely on the eavesdropper's decoding results, the security performance would not be affected by the eavesdropper's decoding means. Numerical results show that the performance of our model is guaranteed whether the eavesdropper learns the decoder himself or uses the legal decoder.

中文翻译：

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高斯窃听通道下基于双重MINE的神经安全通信

近年来，一些研究致力于在高斯窃听信道下端到端学习基于自动编码器的物理层安全通信系统。但是，在这些工作中，不仅通过合法接收者而且还通过窃听者进行必要的解码输出，从而了解了编码器模型的可靠性和安全性。实际上，已知窃听者的解码器或其输出的假设是不切实际的。为了解决这个问题，在本文中，我们提出了一种基于双重互信息神经估计（MINE）的神经安全通信模型。此方法的安全性约束仅由合法和窃听通道的输入和输出信号样本构成，并受益于训练编码器完全独立于解码器。而且，由于安全编码的设计不依赖于窃听者的解码结果，因此安全性能不会受到窃听者的解码方式的影响。数值结果表明，无论窃听者自己学习解码器还是使用合法的解码器，我们模型的性能得到保证。