  Finite-length codes are learned for the Gaussian wiretap channel in an
end-to-end manner assuming that the communication parties are equipped with
deep neural networks (DNNs), and communicate through binary phase-shift keying
(BPSK) modulation scheme. The goal is to find codes via DNNs which allow a pair
of transmitter and receiver to communicate reliably and securely in the
presence of an adversary aiming at decoding the secret messages. Following the
information-theoretic secrecy principles, the security is evaluated in terms of
mutual information utilizing a deep learning tool called MINE (mutual
information neural estimation). System performance is evaluated for different
DNN architectures, designed based on the existing secure coding schemes, at the
transmitter. Numerical results demonstrate that the legitimate parties can
indeed establish a secure transmission in this setting as the learned codes
achieve points on almost the boundary of the equivocation region.
