Secure communication and image encryption using emotional learning-based chaos synchronization

Abstract

In this article, a new synchronization controller based on brain emotional learning with universal approximation property is proposed for secure communication and image encryption. Recently, a brain emotional learning based intelligent controller using Gaussian activation functions has been proposed in which, the universal approximation conditions are satisfied. In this paper, instead of Gaussian functions, Legendre polynomials are utilized, due to fewer adjustable parameters. The synchronization controller is developed using the principles of feedback linearization and the uncertainties are estimated using the proposed emotional learning-based system. In secure communications, sending all state variables of the master system through the communication channel is not permitted. Thus, an observer is designed in the slave system to estimate the required signals. The performance of the proposed method in message recovery in the presence of observer and encryptor is examined in this paper. Furthermore, the effectiveness of the suggested approach in image encryption based on different security analyses such as histogram analysis, correlation coefficient analysis, entropy analysis, original image and encrypted image discrimination, salt and pepper noise attack, and key sensitivity analysis, is evaluated. Simulation results verify 6% reduction in the mean square error (MSE) of the synchronization process due to using Legendre polynomials instead of Gaussian functions. Moreover, in comparison with some previous related works, the aforementioned security indices for the proposed method are more ideal.