Differential evolution optimization of intertwining logistic map-DNA based image encryption technique

Differential evolution (DE) is a powerful evolutionary algorithms, widely applied in different fields of science and engineering for solving the problem of optimization. Since image encryption has been viewed as an interesting research topic by many experts and innumerable methods to encrypt images have emerged, currently, the focus is on obtaining optimized images. The paper presents a novel image encryption scheme that uses intertwining logistic map (ILM), DNA encoding and DE optimization. The proposed approach is based on three phases: permutation involving ILM, diffusion engaging DNA and optimization using DE. Parameters like entropy, key sensitivity, secret key space, unified average change in intensity (UACI), correlation coefficient -vertical, horizontal and diagonal, and number of pixel change rate have been evaluated to test the efficiency of the proposed method. The paper also compares this performance with that of the genetic algorithms (GA), used previously for optimization. The significance of this approach is enhancing entropy, the essential characteristic of randomness, resisting against numerous statistical and differential attacks and generating good experimental results. The main contribution of this paper is to present the efficiency of DE in image optimization and exhibit how DE is better than GA.