Entropy-Based Optimization in Chaotic Image Encryption Algorithms with Implementation of Artificial Intelligence

This paper addresses the challenge of determining optimal parameters in chaotic systems used for image encryption algorithms based on chaos theory. A baseline algorithm employing a third-order Lorenz chaotic system is examined, incorporating core procedures such as permutation (shuffling) and diffusion. Graphical results are presented to illustrate the variation of image entropy in relation to changes in system parameters. The analysis reveals a distinct region in the parameter space where entropy reaches its highest values. Based on these observations, an optimality criterion is formulated, defining an objective function that captures the entropy’s sensitivity to two key system parameters, including the bifurcation parameter. A complex objective function is derived, and the optimization problem is solved using a modified version of the Price algorithm enhanced with artificial intelligence techniques. The proposed modification demonstrates superior performance in locating the global extremum of the objective function, resulting in enhanced security of the encrypted image. Numerical and graphical results for various images are provided, along with a comparative analysis between the standard and the modified Price method.