Performance Analysis of Chaotic Neural Network and Chaotic Cat Map Based Image Encryption

Abstract

Nowadays, chaotic systems are used quite often, especially in image encryption applications. Hypersensitivity to the initial conditions, limited field-changing signs and irregular movements make chaotic systems one of the critical elements in scientific matters such as cryptography. Chaotic systems are divided in two parts as discrete time and continuous time in terms of their dimensions and properties. Gray level image encryption applications generally use one-dimensional and color image encryption applications generally use multi-dimensional chaotic systems. In this study, Tent Map, Cat Map, Lorenz, Chua, Lu chaotic systems were used for chaotic neural network based image coding application and Logistic Map and 3D Cat Map chaotic systems were used for 3D chaotic Cat Map based image encryption application. The encrypted image and the original image were examined by various analysis methods. As a result of the examinations, it is seen that both algorithms give very successful results in key size, key sensitivity, entropy analysis, histogram analysis and correlation coefficient analysis. According to the analysis, it has been shown that the chaotic neural network-based image encryption algorithm is more secure and successful.