Multistability discrete chaotic map and its application of image encryption

Image encryption based on chaotic maps is an important method for ensuring the secure communication of digital multimedia on the Internet. To improve the encryption performance and security of image encryption systems, a new image encryption algorithm is proposed that employs a compound chaotic map and random cyclic shift. First, a new hybrid chaotic system is designed by coupling logistic, ICMIC, Tent, and Chebyshev (HLITC) maps. Comparison tests with previous chaotic maps in terms of chaotic trajectory, Lyapunov exponent, and approximate entropy illustrate that the new hybrid chaotic map has better chaotic performance. Then, the proposed HLITC chaotic system and spiral transformation are used to develop a new chaotic image encryption scheme using the double permutation strategy. The new HLITC chaotic system is used to generate key sequences used in the image scrambling and diffusion stages. The spiral transformation controlled by the chaotic sequence is used to scramble the pixels of the plaintext image, while the XOR operation based on a chaotic map is used for pixel diffusion. Extensive experiments on statistical analysis, key sensitivity, and key space analysis were conducted. Experimental results show that the proposed encryption scheme has good robustness against brute-force attacks, statistical attacks, and differential attacks and is more effective than many existing chaotic image encryption algorithms.

A new multi-scroll Chua’s circuit with composite hyperbolic tangent-cubic nonlinearity: Complex dynamics, Hardware implementation and Image encryption application

Design and realization of discrete memristive hyperchaotic map with application in image encryption

This paper proposes a new five-dimensional memristive conservative chaotic system (5D MCCS) by introducing a flux-controlled memristor into a conservative chaotic system. Dynamic characteristics of the 5D MCCS are validated through spectral entropy(SE), bifurcation diagrams, Lyapunov exponents (LEs), and Poincaré mappings. Compared with other conservative chaotic systems, the 5D MCCS contains only three nonlinear terms yet can generate complex dynamic characteristics. Additionally, under fixed initial values, parameter variations lead the 5D MCCS to switch between chaotic states and quasi-periodic states. The 5D MCCS exhibits chaotic states over wide parameter and initial value range. Afterwards, the 5D MCCS is realized on Field Programmable Gate Array (FPGA) platform. Finally, the pseudorandom sequences generated by the 5D MCCS have been certified through the National Institute of Standards and Technology (NIST) and are utilized for image encryption.

The security and confidentiality of medical images are of utmost importance due to frequent issues such as leakage, theft, and tampering during transmission and storage, which seriously impact patient privacy. Traditional encryption techniques applied to entire images have proven to be ineffective in guaranteeing timely encryption and preserving the privacy of organ regions separated from the background. In response, this study proposes a specialized and efficient local image encryption algorithm for the medical field. The proposed encryption algorithm focuses on the regions of interest (ROI) within massive medical images. Initially, the Laplacian of Gaussian operator and the outer boundary tracking algorithm are employed to extract the binary image and achieve ROI edge extraction. Subsequently, the image is divided into ROI and ROB (regions outside ROI). The ROI is transformed into a row vector and rearranged using the Lorenz hyperchaotic system. The rearranged sequence is XOR with the random sequence generated by the Henon chaotic map. Next, the encrypted sequence is arranged according to the location of the ROI region and recombined with the unencrypted ROB to obtain the complete encrypted image. Finally, the least significant bit algorithm controlled by the key is used to embed binary image into the encrypted image to ensure lossless decryption of the medical images. Experimental verification demonstrates that the proposed selective encryption algorithm for massive medical images offers relatively ideal security and higher encryption efficiency. This algorithm addresses the privacy concerns and challenges faced in the medical field and contributes to the secure transmission and storage of massive medical images.

The rapid development of digital image transmission technology requires a more secure and effective image encryption scheme to provide the necessary security. This paper introduces an improved sine-transform-based chaotic system (ISTBCS). By linearly weighting and nonlinearly multiplying the selected classic maps, the new chaotic maps with more complex dynamics are obtained through sinusoidal transformation. Performance evaluation shows that it has chaotic characteristics such as wide range of chaos, high complexity, and strong non-periodicity. On this basis, an image encryption algorithm that uses bidirectional pixel encoding, displacement and diffusion at the same time is designed. By pixel encoding the plaintext, the small changes in the ordinary image can be propagated to all the pixels of the encrypted image, which greatly improves the ability to resist known plaintext attacks and selected plaintext attacks. Simulation results and performance analysis show that the algorithm has a large key space, strong key sensitivity, strong performance to make the structural data correlation disappear, and then can increase the entropy, has good anti attack ability, and can effectively meet the needs of image encryption.

Generating grid double-scroll attractors from magnetized SC-CNN and its application in image encryption

This paper presents a novel discrete memristor model that incorporates exponential and absolute value functions. A discrete coupled memristor neural network model is constructed based on this memristor design. The periodic and chaotic regions of the discrete neural network model are determined using bifurcation and Lyapunov exponent spectrum. Furthermore, by varying the initial values of the discrete memristive neural network, we observe the coexistence of chaos and periodic attractors, as well as periodic attractors. Additionally, an application to color image encryption based on the discrete system model is given. Security analysis is conducted in the aspects of key space, histogram analysis, correlation analysis, sensitivity analysis, Peak Signal-to-Noise Ratio (PSNR), and information entropy analysis. The analysis results show that the algorithm has a key space size of [Formula: see text], and the information entropy of baboon graph is 7.9993, which is very close to the ideal value of 8. It shows that the image encryption algorithm is feasible and effective. Finally, the implementation of the discrete memristive neural network model is realized using Field Programmable Gate Array (FPGA). The experimental implementation is conducted using the Verilog language on the Vivado 2018.3 platform, and the obtained results align with the numerical simulation results obtained through MATLAB.

A variable-order fractional memristor neural network: Secure image encryption and synchronization via a smooth and robust control approach

Due to easy and simple implementation, normally single 1-D chaotic maps like logistic and sine maps are employed in multimedia data encryption. However, data encrypted through a single chaotic map does not provide better security in terms of resistance against various attacks. In this paper, 2D Henon chaotic map and skew tent map are deployed in the design of an efficient chaos-based image encryption algorithm. To confuse the relationship between plaintext and ciphertext images, both chaotic maps play a key role in the permutation and diffusion mechanism. In the confusion stage, firstly, the Henon chaotic map generates two different chaotic sequences, which are further applied in row and column permutation of plaintext image. The pixel values diffusion is produced by unimodal skew tent map via XOR operations. In the last stage of encryption algorithm, Hussain's substitution box is used to substitute each pixel into a new random pixel. Extensive security analysis and resistance to statistical attack prove the security of anticipated scheme.

A color image encryption scheme based on 1D cubic map

An n-dimensional polynomial modulo chaotic map with controllable range of Lyapunov exponents and its application in color image encryption

A novel devaney chaotic map with uniform trajectory for color image encryption

The widespread adoption of consumer electronics (CE), particularly in smart home ecosystems, has led to the proliferation of intelligent devices such as smart cameras, video doorbells, and smart door locks. While these devices enhance convenience and automation, they generate massive amounts of visual data containing sensitive personal information, thereby increasing the risk of privacy leakage and challenging traditional encryption methods in consumer IoT networks. To address this issue, a cryptanalysis-driven image encryption scheme via a chaos-based 3D S-box and genetic code is proposed, specifically designed to safeguard visual privacy in consumer applications. First, a novel dynamical architecture, formally termed 3D Non-degenerate Hyperchaos (3D-NDHC), is constructed to serve as a robust entropy source. Explicitly defined as a globally bounded volume-expanding hyperchaotic system, 3D-NDHC distinguishes itself from traditional dissipative maps by exhibiting global robust hyperchaos and scalable complexity, where the Lyapunov exponents grow logarithmically with control parameters, ensuring high unpredictability. Next, a dynamically generated three-dimensional S-box driven by these chaotic sequences is introduced to perform efficient image permutation. Subsequently, a genetic code method enhanced by cryptanalysis is applied to confuse the permuted image matrix, specifically addressing the security vulnerabilities often inherent in lightweight encryption algorithms. Finally, under the control of pseudo-chaotic sequences, a lightweight wave diffusion process is employed to spread the pixel values and produce the final ciphertext image. In contrast to conventional algorithms that rely on standard encryption techniques, the proposed approach integrates a chaos-driven 3D S-box with a cryptanalysis-enhanced genetic code scheme. This combination introduces a high level of confusion and diffusion, significantly increasing the difficulty for attackers attempting to illegally decrypt sensitive images from smart devices. Experimental results and security analyses demonstrate that the scheme effectively resists various cryptographic attacks and outperforms several recently proposed image encryption algorithms, offering a robust solution for protecting digital image privacy in smart home environments.

Based on the one-dimensional Sine map and the two-dimensional Henon map, a new two-dimensional Sine-Henon alteration model (2D-SHAM) is hereby proposed. Basic dynamic characteristics of 2D-SHAM are studied through the following aspects: equilibria, Jacobin eigenvalues, trajectory, bifurcation diagram, Lyapunov exponents and sensitivity dependence test. The complexity of 2D-SHAM is investigated using Sample Entropy algorithm. Simulation results show that 2D-SHAM is overall hyperchaotic with the high complexity, and high sensitivity to its initial values and control parameters. To investigate its performance in terms of security, a new 2D-SHAM-based image encryption algorithm (SHAM-IEA) is also proposed. In this algorithm, the essential requirements of confusion and diffusion are accomplished, and the stochastic 2D-SHAM is used to enhance the security of encrypted image. The stochastic 2D-SHAM generates random values, hence SHAM-IEA can produce different encrypted images even with the same secret key. Experimental results and security analysis show that SHAM-IEA has strong capability to withstand statistical analysis, differential attack, chosen-plaintext and chosen-ciphertext attacks.