Improved Chaotic Logistic Map Algorithm based on Bio-Inspired Algorithm for Image Encryption

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Ajay Kumar et. al


In this paper, a bio-inspired black widow optimization algorithm has been employed to determine the optimal parameter values of the chaotic logistic map algorithm for image encryption. The Black widow optimization algorithm is based on the mating behaviour of black widow spiders and it provides early convergence over the other algorithms due to an exclusive stage known as cannibalism. This stage removes the inappropriate solutions in each iteration. The proposed method employs the traditional confusion and diffusion architecture. Initially, in the proposed method, optimal parameter values of the chaotic logistic map algorithm are determined using the Black Widow optimization algorithm and a random key is generated. After that, an Exclusive-OR operation is performed between the secret image and a random key to achieve confusion. Next, to achieve diffusion, the image matrix is randomly circular shifted horizontally and vertically. The simulation evaluation is performed on standard dataset images. Further, subjective and objective analyses are performed for the proposed method to evaluate its performance over the existing methods. At last, comparative analysis was done with the existing methods and it was found that the proposed method provides better entropy and number of pixel change rates than the existing methods. 

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