With advancements in computer and communication technologies, the production, utilization and applications of digital images is at an unprecedented rate. Recent applications include military communications, remote sensing, novel engineering designs storage and communications, as well as medical imaging. In most cases, such images convey highly sensitive or confidential information, which creates a strong need for the design of secure and robust color image cryptosystems. Recent literature has shown that fractional-order functions exhibit improved performance over their corresponding integer-order versions. This is especially true in their use in image processing applications. In this research work, we make use of a four-dimensional (4D) hyperchaotic Chen map of fractional-order, in conjunction with a sine chaotic map and a novel hybrid DNA coding algorithm. A thorough numerical analysis is presented, showcasing the security performance and efficiency of the proposed color image cryptosystem. Performance is gauged in terms of resilience against visual, histogram, statistical, entropy, differential, as well as brute-force attacks. Mean values of the metrics computed are as follows. MSE of 9396, PSNR of 8.27 dB, information entropy of 7.997, adjacent pixel correlation coefficient of 0, NPCR of 99.62%, UACI of 33, MAE of 80.57, and a very large key space of 2 ⁷⁴⁴ . The proposed image cryptosystem exhibits low computational complexity, as it encrypts images at a rate of 4.369 Mbps. Furthermore, it passes the the NIST SP 800 suite of tests successfully. Comparison of the computed metrics of the proposed image cryptosystem against those reported in the state-of-the-art by counterpart algorithms show that the proposed cryptosystem exhibits comparable or superior values.

This work introduces a novel image encryption algorithm specifically designed for the protection of color medical images, particularly crucial in the era of cloud storage, where security and privacy are paramount. The initial phase of the encryption process leverages a substantial array of Fibonacci Q-matrices to manipulate image data intricately. Following this, a unique Substitution box (S-box) transformation, developed in the Galois field (2 ⁸ ), is applied to each of the RGB channels, further enhancing the security layers. The final phase employs an encryption key generated from a hyperchaotic system of differential equations that models a memristive coupled neural network, offering a high degree of unpredictability and resistance to attacks. Performance metrics show high security [Peak Signal-to-Noise Ratio (PSNR) of 8.21 dB, Mean Absolute Error (MAE) of 81.72, and ≈ 0 Pixel Cross-Correlation (PCC)], resistivity to occlusion and noise attacks, an enormous key space of 2 ⁶¹¹² , and with savvy parallel processing techniques, a high encryption rate of 16.65 Mbps. The integration of this encryption algorithm into cloud storage systems is of significant importance, as it ensures secure and confidential handling of sensitive medical images, addressing the growing concerns about data breaches and unauthorized access in the healthcare sector. Compared to previous color image encryption techniques, this work provides a large key space of 2 ⁶¹¹² and a small encryption time per image of around 0.1 second for encrypting 16 images at once. Thus, this approach is suitable for medical imaging techniques, such as Computed Tomography (CT) scans that generate multiple images of the tissues for the diagnosis of the patient.

Image encryption is essential for protecting sensitive visual data during transmission and storage. This article introduces a novel image encryption technique that utilizes three hyperchaotic systems: a memristor system, a hyperchaotic 7D system, and an Erbium-doped fiber laser-based system. The algorithm takes advantage of the complex dynamics and random-like behavior of these systems to implement a robust three-layer encryption process. In each layer, a pseudo-random number generator is used to construct an S-box and an encryption key, which are subsequently applied to the image data. The proposed encryption scheme is implemented in software using Wolfram Mathematica® version 13.1 and in hardware using an AMD Virtex-7 FPGA VC707, achieving encryption rates of 9.7 Mbps and 0.67 Gbps, respectively. Experimental results demonstrate the scheme’s effectiveness in terms of encryption quality, resistance to various cryptanalysis attacks, and a high computational efficiency. The proposed method shows significant potential for secure image transmission and storage applications.

This article presents a novel augmented image encryption algorithm tailored for securing satellite images, addressing the critical need for robust protection of sensitive geographic data. Implementing Shannon’s principles of confusion and diffusion, the method begins by augmenting multiple plain images into a single large image, followed by a three-stage encryption process. Initially, the augmented image is separated into its three color channels, which are transformed into one-dimensional (1D) bit-streams, split and altered using the Gauss Circle map, and restructured via Fredkin Gates to enhance unpredictability. Subsequently, the bit-streams are converted into 1D bytes and $$2 \times 2$$ matrices, processed through three systems incorporating hyperchaos-induced keys and dynamic Hill Cipher matrices for additional confusion and diffusion. The final stage combines these encrypted streams into one image while preserving the integrity of color data. The proposed method achieves strong security metrics, including an average Number of Pixels Change Rate (NPCR) of $$99.6115\%$$ , a Unified Average Changing Intensity (UACI) of $$31.71\%$$ , and high entropy values (e.g., 7.9989) for encrypted images, ensuring robust resistance to differential and statistical attacks. The encryption demonstrates computational efficiency with an encryption time of 0.2817s for $$256\times 256$$ images and maintains a low Peak Signal-to-Noise Ratio (PSNR) of 8.1 dB, reflecting effective data obfuscation. This multistage chaos-based approach, leveraging Fredkin logic gates and hyperchaos-induced keys, significantly enhances security, scalability, and efficiency, making it ideal for high-stakes satellite imagery applications where data integrity and confidentiality are paramount.

This study presents a sophisticated encryption methodology specifically designed for the secure transfer of medical images across cloud services. The initial phase of the algorithm involves the consolidation of multiple images to form a single augmented image, which is then subjected to the first layer of encryption. This layer employs an encryption key and an S-box generated through a Memristive Coupled Neural Network Model (MCNNM), establishing a strong foundation for security. Following this, the novel integration of Moore’s Automaton with Gold sequences is applied as a confusion mechanism, intrinsically scrambling the image structure to effectively disrupt pixel correlations. The encryption process iterates over N cycles, significantly deepening the level of encryption with each iteration. Performance evaluations reflect a considerable key space of $$2^{2020}$$ and a high encryption rate of 15.5 Mbps, while rigorous statistical tests validate the algorithm’s resilience. The encryption system proposed in this manuscript not only ensures a formidable level of security but is also pragmatically designed for application in the protection of sensitive healthcare data.

Secure dissemination of high-resolution satellite imagery remains challenging because many image-tailored ciphers either (i) emphasize permutation-heavy designs without sufficiently strong, plaintext-adaptive nonlinearity, or (ii) provide strong security metrics but fall short on scalable, near-real-time performance and robustness assessment under realistic channel impairments. To address these gaps, this work proposes a three-stage chaos-chess hybrid encryption pipeline for color satellite images that couples fractional-order hyperchaotic key generation with lightweight algebraic mixing, dynamic substitution, and structured bit-level diffusion. First, multiple images are optionally augmented and each RGB channel is partitioned into $$2\times 2$$ pixel matrices that are mixed via invertible matrices derived from a 6D fractional-order hyperchaotic Vaidyanathan system, providing efficient confusion suitable for parallelization. Second, plaintext-sensitive S-boxes are constructed online from a 4D fractional-order hyperchaotic system and applied per channel to enhance nonlinearity and satisfy stringent criteria (NL $$=108$$ , SAC $$\approx 0.5$$ , low LAP and DAP). Third, the resulting bit-streams are diffused by traversing $$8\times 8$$ blocks using Knight’s Tour paths and XORing with 4D hyperchaotic key-streams to amplify avalanche propagation. Experiments on satellite and natural images demonstrate high ciphertext randomness (entropy $$\approx 7.999$$ ), strong differential resistance (NPCR $$\approx 99.62\%$$ , UACI $$\approx 30.95\%$$ ), near-zero adjacent-pixel correlation (PCC $$\approx 0$$ ), and a large key space ( $$>2^{3827}$$ ), while measured runtimes indicate suitability for real-time or near-real-time operation. Noise-like ciphertexts and lossless recovery are verified via visual, histogram, and DFT analyses, and robustness under occlusion and noise attacks (salt-and-pepper, Gaussian) is evidenced. The resulting modular design provides a scalable pathway for protecting remote sensing data and supports future integration with ROI-aware processing and hardware acceleration.