Image encryption using a standard map and a teaching-learning based optimization algorithm

Image encryption is a topic that has been the subject of numerous articles and dissertations in recent years. A proper encryption algorithm has high speed and can withstand statistical and differential attacks. The image digest gives a different output for each image, and only by changing one bit in the image, the output changes completely. Therefore, the image digest can be used as a suitable option for the initial values of the chaotic map. In this paper, the images are encrypted using the Teaching-Learning Based Optimization (TLBO) algorithm. First, the image digest is calculated by SHA-512 and used to generate numbers between zero and one. The image is then divided into 16 equal parts. In the next step, the image pixels are shuffled using the special mode of the standard map developed in this article. Then each of the parts becomes a teacher once. The best value, one that improves the entropy, is selected among the generated values. The other parts follow this value, and the initial value of the chaotic function is obtained for all 16 parts. Then the encryption of each part is done by the logistic map. This algorithm has relatively good execution time and has demonstrated good results against statistical and differential attacks. Among the most important results obtained for the proposed method, reaching the value of 35.5 for UACI, reaching the value of 99.6 for NPCR, and reaching the value of 7.971 for the information entropy can be mentioned.