An Efficient Optimal Key Based Chaos Function for Medical Image Security

With the rapid development in the field of medical image encryption, researchers analyzed a number of encryption algorithms based on chaotic systems. But there is a difficulty lies, i.e., small key space and weak security in 1-D chaotic cryptosystems. To overcome this difficulty, an alternative security model was proposed in this paper. This paper investigated highly secure medical images with a couple of subkeys, initially where a couple of subkeys are given by utilizing chaotic logistic and tent maps. As per the chaotic (C-function) process, the security was investigated like diffusion as well as confusion. Based on the initial conditions, different random numbers were generated for each map from chaotic maps. Adaptive grasshopper optimization algorithm with PSNR and correlation coefficient fitness function was proposed to choose the optimal secret and public key of the system among the random numbers. The reason behind choosing adaptive process is to enhance high-security investigation of the current proposed model compared to the existing methods. At last, the proposed strategy results were compared with existing security methods and literary works, but found to be high performing.