A Novel Image Encryption Algorithm Based on the Delayed Maps and Permutation-Confusion-Diffusion Architecture

Abstract

To improve the dynamical behaviors of 1D chaotic maps, a new linear-delay-modulation method (LDM) is proposed. Derived from the Sine map, a delayed Sine map (DSM) is proposed based on the LDM. Then, we substitute the Sine map in the SIMM system with DSM and obtained a delayed SIMM system (DSIMM). Its chaotic performance is analyzed through the phase diagram, Lyapunov exponent spectrum, and complexity. The results show that the delayed chaotic map can generate more complex dynamical behaviors and more random sequences. Hence, we apply the two delayed systems to a novel image encryption algorithm with the permutation-confusion-diffusion architecture. Firstly, to permutate the pixel of the image efficiently, the plain-image is scrambled by using a multilayer of the nonlinear index. Secondly, the image is confused by using the chaotic matrix generated with two chaotic sequences, and then, the ciphertext is transformed into a 1D sequence. Finally, to improve the plaintext sensitivity and facilitate key management, we enhance the sensitivity by applying a novel diffusion algorithm instead of using plaintext-related keystream. The diffusion equation contains the sum of undiffused pixels and the operation of cyclic bit-shift. Simulation results for the gray image illustrate the effectiveness of the proposed encryption algorithm.

Application of Data Image Encryption Technology in Computer Network Information Security

“…Today, RSA is still one of the most popular public key cryptosystems. At the same time, the US National Bureau of Standards (NBS) recognized the important commercial use of cryptography, so it solicited and published the US data encryption standard DES in 1977 [16].…”

A selective image encryption scheme using LICC hyperchaotic system

“…The number of pixel change rates (NPCR) and unified average changing intensity (UACI) are two common indicators used to analyze the ability to resist differential attacks [31]. The NPCR and UACI can be defined as…”

“…Then the same encryption scheme is performed on these two images, and the encryption algorithm can be summarized by analyzing the correlation and difference between the two encrypted images to achieve the purpose of deciphering. The number of pixel change rates (NPCR) and unified average changing intensity (UACI) are two common indicators used to analyze the ability to resist differential attacks [31]. The NPCR and UACI can be defined as $$\begin{equation} \def\eqcellsep{&}\begin{array}{l} NPCR = \frac{{\sum_{i = 1}^M {\sum_{j = 1}^N {D(i,j)} } }}{{M \times N}} \times 100\% ,\\[18pt] UACI = \frac{{\sum_{i = 1}^M {\sum_{j = 1}^N {\left| {{C_1}(i,j) - {C_2}(i,j)} \right|} } }}{{255 \times M \times N}} \times 100\% ,\\[10pt] D(i,j) = \left\{ \def\eqcellsep{&}\begin{array}{l} 1,{C_1}(i,j) \ne {C_2}(i,j)\\[6pt] 0,{C_1}(i,j) = {C_2}(i,j) \end{array} \right.. \end{array} \end{equation}$$where $${C_1}(i,j)$$ represents the value of the pixel in row i and column j after the original image is encrypted.…”

A New Image Encryption Technique Based on Sine Map, Chaotic Tent Map, and Circulant Matrices

“…e introduction of the sine map enhances the time of iteration of a chaotic system. Due to an increase in the time of iteration, the security of the image increases [24]. e main goal of this work is to make advanced venture in the regime of image encryption using chaotic maps.…”