Digital Cryptography Implementation using Neurocomputational Model with Autoencoder Architecture

Francisco Quinga-Socasi, Ronny Velastegui, Luis Zhinin-Vera, Rafael Valencia-Ramos, Francisco Ortega-Zamorano, Oscar Chang

Abstract: An Autoencoder is an artificial neural network used for unsupervised learning and for dimensionality reduction. In this work, an Autoencoder has been used to encrypt and decrypt digital information. So, it is implemented to code and decode characters represented in an 8-bit format, which corresponds to the size of ASCII representation. The Back-propagation algorithm has been used in order to perform the learning process with two different variant depends on when the discretization procedure is carried out, during (model I) or after (model II) the learning phase. Several tests were conducted to determine the best Autoencoder architectures to encrypt and decrypt, taking into account that a good encrypt method corresponds to a process that generate a new code with uniqueness and a good decrypt method successfully recovers the input data. A network that obtains a 100% in the two process is considered a good digital cryptography implementation. Some of the proposed architecture obtain a 1

External IDs:dblp:conf/icaart/Quinga-SocasiVZ20