Image Encryption Method based on Chaotic Maps and Metaheuristic Walrus Optimization Algorithm

In this study, an image encryption method is presented by determining the best parameters of the chaotic map for encryption purposes using the metaheuristic walrus optimization. The chaotic maps were used for generating the substitution box, key, and permutation index. The main novelty of the proposed method is that a multi-objective function was designed by considering the multiple security parameters such as entropy, CC, and SSIM. The key finding of this study is that the histogram of the encrypted image is equally distributed and its validation is done using the chi-square test. The result indicates that the proposed method has passed the chi-square test for the different images. Furthermore, the security metrics, PSNR, entropy, CC, SSIM, NPCR, and MD, define that the proposed method accomplishes the desired parameter values required in the encryption process. Furthermore, we found that the metaheuristic algorithm effectively determines the optimal parameter values for different secret images, and these parameter values are not fixed. The comparative analysis with the previous approaches shows that the proposed method achieves a high value of entropy, NPCR, and a lower value of the PSNR due to finding the initial parameter of the chaotic map based on the multi-objective function using the Walrus optimization. From the application perspective, the proposed method is suitable for communicating the secret image in several applications, namely, healthcare, IoT, and defense, for securing the sensitive information of the users. The limitation of the presented method is that the optimal parameters of the chaotic map must be communicated to the receiver in order to decrypt the encrypted image. Furthermore, the time complexity is dependent on the secret image resolution and parameter values of the metaheuristic Walrus optimization algorithm.

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