  Echo state networks are simple recurrent neural networks that are easy to
implement and train. Despite their simplicity, they show a form of memory and
can predict or regenerate sequences of data. We make use of this property to
realize a novel neural cryptography scheme. The key idea is to assume that
Alice and Bob share a copy of an echo state network. If Alice trains her copy
to memorize a message, she can communicate the trained part of the network to
Bob who plugs it into his copy to regenerate the message. Considering a
byte-level representation of in- and output, the technique applies to arbitrary
types of data (texts, images, audio files, etc.) and practical experiments
reveal it to satisfy the fundamental cryptographic properties of diffusion and
confusion.
