A novel multi-image cryptosystem based on weighted plain images and using combined chaotic maps

Abstract

This paper introduces a new multi-image cryptosystem based on modified Henon map and nonlinear combination of chaotic seed maps. Based on the degree of correlation between the adjacent pixels of the plain image, a unique weight is assigned to the plain image. First, the coordinates of plain images are disrupted by modified Henon map as confusion phase. In the first step of diffusion phase, the pixels content of images are changed separately by XOR operation between confused images and matrices with suitable nonlinear combination of seed maps sequences. These combination of seed maps are selected depending on the weight of plain images as well as bifurcation properties of mentioned chaotic maps. After concatenating the matrices obtained from the first step of diffusion phase, the bitwise XOR operation is applied between newly developed matrix and the other produced matrix from the chaotic sequences of the Logistic-Tent-Sine hybrid system, as second step of diffusion phase. The encrypted image is obtained after applying shift and exchange operations. The results of the implementation using graphs and histograms show that the proposed scheme, compared to some existing methods, can effectively resist common attacks and can be used as a secure method for encrypting digital images.

An efficient image cryptosystem based on the residue number system and hybrid chaotic maps

“…Encryption algorithms with a large enough key space can well resist various violent attacks such as exhaustive attacks [2]. An effective encryption scheme should have a key space more than 2 100 to resist brute force attacks [11,43]. In this study, the secret keys are a, b, c, d, e, f, r in proposed RNS, 𝑢 0 in the Sine map; 𝑣 0 in the Logistic map; g, h, and k (number of iteration) in the ACM.…”

“…First, a very small change (in the range of 10 -14 ) in the secret key generates completely different encrypted image. Second, the decryption process with an incorrect key, which is slightly different (in the range of 10 -14 ) from the correct key, does not reconstruct the original image [11]. The key sensitivity simulation results are shown in Fig.…”

“…The SNR value is calculated using equation 16 [11]. Where M is the original image, and N is decrypted image after cropping attacks.…”

“…Although high dimensional (HD) chaotic maps offer high security, they are complicated in terms of implementation and computation load. Among the chaotic maps, Logistic, Henon, Sinusoidal, Tent, Arnold's cat map, and Baker and Lorenz maps are well-known [6,11].…”

“…As such, cryptography can get the benefit of this characteristic of chaotic maps to change the coordinates (Confusion) and the content (Diffusion) of the pixels of an image [6]. Although one-dimensional (1D) chaotic maps can be easily implemented, they are vulnerable to attacks [11]. Although high dimensional (HD) chaotic maps offer high security, they are complicated in terms of implementation and computation load.…”