  End-to-end learning of communication systems with neural networks and
particularly autoencoders is an emerging research direction which gained
popularity in the last year. In this approach, neural networks learn to
simultaneously optimize encoding and decoding functions to establish reliable
message transmission. In this paper, this line of thinking is extended to
communication scenarios in which an eavesdropper must further be kept ignorant
about the communication. The secrecy of the transmission is achieved by
utilizing a modified secure loss function based on cross-entropy which can be
implemented with state-of-the-art machine-learning libraries. This secure loss
function approach is applied in a Gaussian wiretap channel setup, for which it
is shown that the neural network learns a trade-off between reliable
communication and information secrecy by clustering learned constellations. As
a result, an eavesdropper with higher noise cannot distinguish between the
symbols anymore.
