  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.
