An improved Logistic chaotic map and self-adaptive model for image encryption

An image encryption scheme provides the means for securely transmitting images over public channels. In this paper, I proposed an efficient permutation-diffusion image encryption scheme based on an improved Logistic chaotic map (ILCM) and self-adaptive model. The permutation phase permuted the addresses of the image pixel using the first chaotic sequence generated by ILCM, created the parameter which was related to the number of 0's in the bit-planes of the plain-image. Since this different plain-image had different parameter for permutation even just only a bit is changed in the plain-image. The diffusion phase then altered the pixel values of the permuted image with combined space-bit-plane exchange and XOR operation with the second chaotic sequence generated by ILCM. Simulations and performance evaluations showed that the ILCM with larger chaotic ranges and better chaotic behaviors compare with the Logistic chaotic map (LCM). Numerical experiments also showed that our proposed self-adaptive method can considerably enhance the cryptosystem resistance against known/chosen-plaintext and differential attacks. Theoretical analysis and computer experiments confirmed that the new algorithm was secure and reliable, with high potential to be adopted for network security and secure communications.