An Efficient Hybrid Optimization Algorithm with Elliptic-Curve Cryptography for Image Encryption
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 7, Pages 4753-4764
Abstract: In recent times, there is an exponential growth in the number of digital images being produced and there is a need to protect the images over the attacked for secure data transmission in the network. Cryptography is found to be a simple and effective way used to protect multimedia data. At the same, the optimal key generation process in cryptography is treated as an optimization problem and it can be resolved by the use of metaheuristic algorithms. Therefore, this study mainly focuses on the design of metaheuristic optimization algorithm based cryptographic encryption for image security. In this view, this paper develops an efficient hybridization of Cat Swarm optimization algorithm (CSO) and fruit fly optimization (FFO) algorithm with ECC for image security, named CSO-FFO-ECC model. The proposed model performs the encryption and decryption process using ECC technique. Besides, in order to reduce the computation time needed for the random selection of the public and private keys, the proposed model uses a hybridization of CSO and FFO algorithm for optimal key selection. It treats the ‘fitness function’ as max key with PSNR for scrambling and unscrambling the unscramble the images. The experimental validation of the CSO-FFO-ECC model takes place on benchmark test images. The results are evaluated with respect to peak signal to noise ratio (PSNR) and mean square error (MSE).
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