Improved security of neural cryptography using don´t-trust-my-partner and error prediction

Neural cryptography deals with the problem of key exchange using the mutual learning concept between two neural networks. The two networks will exchange their outputs (in bits) so that the key between the two communicating parties is eventually represented in the final learned weights and the two networks are said to be synchronized. Security of neural synchronization depends on the probability that an attacker can synchronize with any of the two parties during the training process, so decreasing this probability improves the reliability of exchanging their output bits through a public channel. This work proposes an exchange technique that will disrupt the attacker confidence in the exchanged outputs during training. The algorithm is based on one party sending erroneous output bits with the other party being capable of predicting and removing this error. The proposed approach is shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize.