Enhancing image encryption using chaotic maps: a multi-map approach for robust security and performance optimization

This paper proposes a model for encrypted images that depend on chaotic maps. This scheme uses eight chaotic maps to perform the encryption process: Logistic, Gauss, Circle, Sine, Singer, Piecewise, Tent, and Chebyshev. The two major processes of the suggested model are chaotic confusion and pixel diffusion. Chaotic maps are used to permute the pixel positions during the confusion process. In the diffusion process, the value of the image pixel is changed. To evaluate the suggested model, some performance metrics were used, such as execution time, peak signal-to-noise ratio, entropy, key sensitivity, noise attack, the number of pixels change rate (NPCR), unified average changing intensity (UACI), histogram analysis, and cross-correlation. According to experimental analysis, images encrypted with the suggested system have correlation coefficient values that are almost zero, NPCR of 99.6%, UACI of 32.9%, the key space of 10^(80), the histogram analysis showed that the encrypted images have almost similar pixels, an execution time of 0.1563 ms, the, and entropy of 7.9973. All prior results have verified the robustness and efficiency of the suggested algorithm.