Image Encryption Research Articles

An Image Encryption Algorithm Based on Tabu Search and Hyperchaos

In this paper, we propose a digital image encryption scheme based on Tabu Search (TS) algorithm and Chen’s hyperchaos system. First, in order to enhance the security of the algorithm and resist the known-plaintext attack, the key is associated with the ordinary image, and the hash value generated by the ordinary image is used as the initial value of the hyperchaotic system. Moreover, the TS algorithm is used to obtain the optimal subsequence to scramble the sub-block image, which ensures the scrambling effect of the algorithm. In addition, for the sake of minimizing the correlation of neighborhood pixels and strengthening the effect of scrambling, the ordinary image is divided into blocks for scrambling and diffusion. Through simulation and experiments, the key sensitivity, differential attack and pure ciphertext attack are analyzed. Compared with the other encryption schemes, the results verify the effectiveness and reliability of the proposed scheme.

A novel 2D Hyperchaotic Sine Logistic map based image encryption scheme

A novel 2D Hyperchaotic Sine Logistic map based image encryption scheme

Data reorganization for image encryption: presentation in the data of hare

ABSTRACT In track and field, a hare (or pacemaker) is a runner who leads the early part of a race to help other competitors achieve better performance and potentially set records. This practice became common in the early 1980s. In cryptography, organizing data before encryption serves as a strategic technique to strengthen defenses against cryptanalytic attacks. Attackers often exploit patterns in encrypted data to narrow down key possibilities. By deliberately disrupting data structure prior to encryption, we can complicate these efforts. This concept, termed “Presentation in the Data of Hare,” proposes various data organization methods (e.g., line by line, column by column) to enhance encryption. This approach can add 21 bits to the encryption key, significantly increasing the complexity and time required to crack it—equivalent to a 2 million-fold increase. This work focuses on improving security in cryptographic systems by employing innovative techniques, including those related to post-quantum cryptography. By strategically organizing data before encryption, we aim to hinder potential cryptanalytic attacks, making it considerably harder for attackers to exploit any regularities, thus bolstering resilience against both classical and quantum threats.

A high-quality visual image encryption algorithm utilizing the conservative chaotic system and adaptive embedding

Regarding the issue of encrypted images in the channel attracting the attention of attackers and making them vulnerable to attacks. This paper proposes a high-quality visual image encryption algorithm utilizing the conservative chaotic system, two-dimensional compressive sensing, optimization local of binary patterns, and adaptive embedding method. Firstly, a conservative chaotic system with excellent performance and resistance to reconstruction attacks is proposed. Secondly, the pseudo-random sequences generated by the chaotic system dynamically generate measurement matrices, which are optimized before compressing the image. Then, during the encryption process, by utilizing the newly proposed composite DNA computing rules, chaotic sequences dynamically encode and compute image information, which enriches the coding criteria and improves the security of encryption algorithms. Finally, in the information embedding stage, the newly proposed OLBP algorithm can identify important textured and non-textured regions of the host image, prioritize embedding non-textured regions, and then embed textured regions, which can improve the amount of information embedding and reduce damage to the host image. Experimental simulation and analysis exhibit that the encryption algorithm has high security, strong robustness and high efficiency. Meanwhile the imperceptible analysis of the steganographic images is exceed 52 dB, so the algorithm presents a high-quality visual effect of imperceptibility.

Medical image encryption algorithm based on Fresnel zone formula, differential neural networks, and pixel-guided perturbation techniques

This paper proposes an image encryption technique that integrates deep pixel substitution, data-dependent and chaotic pixel perturbation, and differential neural networks. The pixels of the image are manipulated using a deep pixel substitution operation that is based on the Fresnel Zone equation to eliminate the correlations to the input plain image. Additionally, a perturbation process based on pixel values and chaotic noise is applied to further scramble the image. The resulting image is then subjected to a second round of deep substitution. The differential neural network generates blurring codes by incorporating plain pixel blocks and an encryption key, which are subsequently added to the processed image to produce the final ciphered image. The proposed technique’s effectiveness was evaluated on a large dataset that included both medical and non-medical images. Simulation results indicated that the proposed technique was not only efficient but also effective for both medical and non-medical images, and it outperformed state-of-the-art encryption methods in both security properties and computational efficiency.

Research on Image Encryption Method based on the Chaotic Iteration of a Ternary Nonlinear Function

Objective: Considering that some image encryption algorithms have the disadvantages of complex structure and high computational cost, and there are not many commonly used chaotic systems, which are easy to crack by attacks, to solve these problems, this paper proposes an image encryption algorithm based on three-dimensional nonlinear functions to solve these problems. Methods: The algorithm mainly combines the sinusoidal chaotic map with the ternary nonlinear function system to encrypt the image. Firstly, multiple ternary nonlinear function chaotic systems are designed. Then, the function iteration system is changed to invoke the computation of a specific expression under a random number; it is a chaotic sequence generated according to a chaotic mapping such as sine, and then the value of this chaotic sequence is used to select a ternary nonlinear function for iteration to obtain a chaotic sequence. Finally, the chaotic sequence performs the XOR and scrambling operations on the grey image Results: The algorithm has a simple structure, a better encryption effect, and more incredible difficulty deciphering. Moreover, through the phase diagram and bifurcation diagram, it can be seen that the system has good chaotic characteristics Conclusion: The method in this paper is novel; this method is a random variable order composite operation, which can not only be applied to image encryption but also can be used for fractal map generation and so on, and in some other chaotic fields will have a wide range of applications. It has essential research value.

Layer multiplexing and DNA based color image encryption

Abstract A layer multiplexing-based encrypted image scheme for DNA color images is proposed. Firstly, a memristor is added to the Sprott-B chaotic system to make it a fourth-order memristorised hyper-chaotic system. The hyperchaotic system has better pseudo-randomness. The encryption system proposed generates the initial value of the system by SHA3-256 function to achieve “One image, one secret”. The chaotic sequence is generated by a memory hyperchaotic system for encrypting the image. The encryption process includes image zero complements, image chunking, DNA encryption, layer multiplexing, DNA decryption, and improved XOR diffusion. The encrypted images are analyzed by Matlab. The pixel distribution of the encrypted image is verified to be homogeneous by pixel histogram analysis. The encryption system is tested to have good resistance to shear attacks and noise attacks. The encryption system also has a good encryption effect by information entropy test. Experiments show that the encryption system proposed has a short encryption time and decryption time.

A Dynamic-Varying Parameter Enhanced ZNN Model for Solving Time-Varying Complex-Valued Tensor Inversion With Its Application to Image Encryption.

Time-varying complex-valued tensor inverse (TVCTI) is a public problem worthy of being studied, while numerical solutions for the TVCTI are not effective enough. This work aims to find the accurate solution to the TVCTI using zeroing neural network (ZNN), which is an effective tool in terms of solving time-varying problems and is improved in this article to solve the TVCTI problem for the first time. Based on the design idea of ZNN, an error-adaptive dynamic parameter and a new enhanced segmented signum exponential activation function (ESS-EAF) are first designed and applied to the ZNN. Then a dynamic-varying parameter-enhanced ZNN (DVPEZNN) model is proposed to solve the TVCTI problem. The convergence and robustness of the DVPEZNN model are theoretically analyzed and discussed. In order to highlight better convergence and robustness of the DVPEZNN model, it is compared with four varying-parameter ZNN models in the illustrative example. The results show that the DVPEZNN model has better convergence and robustness than the other four ZNN models in different situations. In addition, the state solution sequence generated by the DVPEZNN model in the process of solving the TVCTI cooperates with the chaotic system and deoxyribonucleic acid (DNA) coding rules to obtain the chaotic-ZNN-DNA (CZD) image encryption algorithm, which can encrypt and decrypt images with good performance.

High-fidelity reversible data-hiding technique in encrypted images using pixel value ordering and center folding strategy

High-fidelity reversible data-hiding technique in encrypted images using pixel value ordering and center folding strategy

All-in-One of Image Encryption, Identity Verification, and Photoelectric Watermark Based on Bidirectionally Photoresponsive Transistors

All-in-One of Image Encryption, Identity Verification, and Photoelectric Watermark Based on Bidirectionally Photoresponsive Transistors

A Switching Memory-Based Event-Trigger Scheme for Synchronization of Lur'e Systems With Actuator Saturation: A Hybrid Lyapunov Method.

This article is concerned with the event-triggered synchronization of Lur'e systems subject to actuator saturation. Aiming at reducing control costs, a switching-memory-based event-trigger (SMBET) scheme, which allows a switching between the sleeping interval and the memory-based event-trigger (MBET) interval, is first presented. In consideration of the characteristics of SMBET, a piecewise-defined but continuous looped-functional is newly constructed, under which the requirement of positive definiteness and symmetry on some Lyapunov matrices is dropped within the sleeping interval. Then, a hybrid Lyapunov method (HLM), which bridges the gap between the continuous-time Lyapunov theory (CTLT) and the discrete-time Lyapunov theory (DTLT), is used to make the local stability analysis of the closed-loop system. Meanwhile, using a combination of inequality estimation techniques and the generalized sector condition, two sufficient local synchronization criteria and a codesign algorithm for the controller gain and triggering matrix are developed. Furthermore, two optimization strategies are, respectively, put forward to enlarge the estimated domain of attraction (DoA) and the allowable upper bound of sleeping intervals on the premise of ensuring local synchronization. Finally, a three-neuron neural network and the classical Chua's circuit are used to carry out some comparison analyses and to display the advantages of the designed SMBET strategy and the constructed HLM, respectively. Also, an application to image encryption is provided to substantiate the feasibility of the obtained local synchronization results.

An Efficient Cryptographic Scheme based on Optimized Watermarking Scheme for Securing Internet of Things

In this work, a new efficient cryptographic scheme based on the concept of chaotic map and optimized watermarking scheme is proposed. In the optimized watermarking scheme, a combination of discrete wave transformation (DWT), hessenberg decomposition (HD), and singular value decomposition (SVD) are used. In this, the host image is first broken down into several sub-bands using multi-level DWT, and the resulting coefficients are then fed into HD during the embedding phase. Simultaneous watermark operation is performed on SVD. Finally, the scale factor embeds the watermark into the host image. The Differential evolution method is used to find the best scaling factor for the optimized watermarking scheme. The resulting watermarked image is then encrypted by the session key based scheme. In this scheme for each image encryption, a new random session key will be produced. The presented approach uses 64-bit plaintext and a variable size key that will be decided at the time of encryption for encrypting an image. Since session keys change with each transmission, this approach does not involve extracting and remembering session keys in order to produce subsequent session keys. IoT devices are used to test the developed method for security. The experiment’s findings shows that the suggested method works better than the current scheme in several aspects.

Fractional derivative of Hermite fractal splines on the fractional-order delayed neural networks synchronization

The purpose of this research is twofold. First, the master–slave synchronization of fractional-order neural networks is explored with time delays using aperiodic intermittent control. Then we present a sufficient condition for master–slave synchronization of delayed fractional-order neural networks via average-width intermittent control technique. A numerical simulation is used to demonstrate the efficacy of the derived results. Second, a novel investigation of the Caputo-fractional derivative of Hermite fractal splines is accomplished. Moreover, its box counting dimension is estimated and related with the Caputo-fractional order. Additionally, we propose an image encryption algorithm utilizing the semi-tensor product (STP). The efficiency of the algorithm is evaluated through the application of statistical measures.

Discrete diffusion models with Refined Language-Image Pre-trained representations for remote sensing image captioning

RS image captioning (RSIC) utilizes natural language to provide a description of image content, assisting in the comprehension of object properties and relationships. Nonetheless, RS images are characterized by variations in object scales, distributions, and quantities, which make it challenging to obtain global semantic information and object connections. To enhance the accuracy of captions produced from RS images, this paper proposes a novel method referred to as Discrete Diffusion Models with Refined Language-Image Pre-trained representations (DDM-RLIP), leveraging an advanced discrete diffusion model (DDM) for nosing and denoising text tokens. DDM-RLIP is based on an advanced DDM-based method designed for natural pictures. The primary approach for refining image representations involves fine-tuning a CLIP image encoder on RS images, followed by adapting the transformer with an additional attention module to focus on crucial image regions and relevant words. Furthermore, experiments were conducted on three datasets, Sydney-Captions, UCM-Captions, and NWPU-Captions, and the results demonstrated the superior performance of the proposed method compared to conventional autoregressive models. On the NWPU-Captions dataset, the CIDEr score improved from 116.4 to 197.7, further validating the efficacy and potential of DDM-RLIP. The implementation codes for our approach DDM-RLIP are available at https://github.com/Leng-bingo/DDM-RLIP.

OphGLM: An ophthalmology large language-and-vision assistant

Vision computer-aided diagnostic methods have been used in early ophthalmic disease screening and diagnosis. However, the limited output formats of these methods lead to poor human–computer interaction and low clinical applicability value. Thus, ophthalmic visual question answering is worth studying. Unfortunately, no practical solutions exist before Large Language Models(LLMs). In this paper, we investigate the ophthalmic visual diagnostic interaction problem. We construct an ophthalmology large language-and-vision assistant, OphGLM, consisting of an image encoder, a text encoder, a fusion module, and an LLM module. We establish a new Chinese ophthalmic fine-tuning dataset, FundusTuning-CN, including the fundus instruction and conversation sets. Based on FundusTuning-CN, we establish a novel LLM-tuning strategy to introduce visual model understanding and ophthalmic knowledge into LLMs at a low cost and high efficiency. Leveraging the pre-training of the image encoder, OphGLM demonstrates strong visual understanding and surpasses open-source visual language models in common fundus disease classification tasks. The FundusTuning-CN enables OphGLM to surpass open-source medical LLMs in both ophthalmic knowledge and interactive capabilities. Our proposed OphGLM has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be publicly available at https://github.com/ML-AILab/OphGLM.

A Novel Parameter-Variabled and Coupled Chaotic System and Its Application in Image Encryption with Plaintext-Related Key Concealment.

The design of a chaotic system and pseudo-random sequence generation method with excellent performance and its application in image encryption have always been attractive and challenging research fields. In this paper, a new model of parameter-variabled coupled chaotic system (PVCCS) is established by interaction coupling between parameters and states of multiple low-dimensional chaotic systems, and a new way to construct more complex hyperchaotic systems from simple low-dimensional systems is obtained. At the same time, based on this model and dynamical DNA codings and operations, a new pseudo-random sequence generation method (PSGM-3DPVCCS/DNA) is proposed, and it is verified that the generated pseudo-random sequence of PSGM-3DPVCCS/DNA has excellent random characteristics. Furthermore, this paper designs a novel pixel chain diffusion image encryption algorithm based on the proposed parameter-variabled coupled chaotic system (PVCCS) in which the hash value of plaintext image is associated with the initial key to participate in the encryption process so that the encryption key is closely associated with plaintext, which improves the security of the algorithm and effectively resists the differential cryptanalysis risk. In addition, an information hiding method is designed to hide the hash value of plaintext image in ciphertext image so that the hash value does not need to be transmitted in each encryption, and the initial key can be reused, which solves the key management problem in application and improves the application efficiency of the encryption algorithm. The experimental analysis shows that the chaotic system constructed in this paper is creative and universal and has more excellent chaotic characteristics than the original low-dimensional system. The sequence generated by the pseudo-random sequence generation method has excellent pseudo-random characteristics and security, and the image encryption algorithm can effectively resist differential cryptanalysis risk, showing advanced encryption performance.

NIR-I Activated Orthogonal NIR-IIb/c Emissions in a Lanthanide-Doped Nanoparticle for Fluorescence Imaging and Information Encryption.

Applying the orthogonal principle for distinguishable second near-infrared (NIR-II) emissions has brought new dimensions for ratio fluorescence imaging (RFI) detection and information encryption, deepening the tissue detection depth and improving signal-to-noise ratio and information security. However, the orthogonal NIR-II emissions underlying these advanced optical applications have been reported only in heterogeneous structures and mixtures, limiting their practicality and potential impact. Herein, NIR-I-activated orthogonal NIR-IIb/c (1530/1825nm) emissions nanoparticles (ONNPs) are developed by spatially separated doping of Tm3+ and Er3+ emitter upon switching 808 and 980nm excitations. RFI techniques and orthogonal NIR-II emission ONNPs are used to demonstrate vessel depth detection based on wavelength-dependent optical attenuation properties in tissue. The superiority of the optical coding and encoding process in a 4×1 binary matrix is demonstrated for anticounterfeiting and decryption imaging of quick-response (QR) code for information storage. The research progress of this NIR-II orthogonal emissions probe will drive the development of biomedical sensing, imaging safety, and future biophotonics technologies.

Analysis of Image Encryption using Triple DES

Abstract: Keeping your essential data secure in today’s world is very essential. There are different types of cyber-attacks which attackers can use to exploit your personal or confidential data for their own needs. Cryptography and its algorithms in cybersecurity has been an important part in providing secure methods to hide or encrypt information or sensitive data which only the person who was intended to be send can access it. This paper presents the triple Data Encryption Standard (DES) approach for secure image encryption from malicious users. This paper provides an efficient, easy and secure approach for encryption of images which is essential in today’s scenario.

Image compression encryption algorithm combining two-dimensional modular hyperchaotic map and compressed sensing

Recently, to offer better ensure for image privacy security, numerous new image encryption algorithms have been proposed. However, these algorithms still suffer from the problems of chaotic performance scarcity, low encryption effect, and high consumption of computational resources. To solve the above issues, we first construct a two-dimensional modular hyperchaotic map (2D-MHM). Then, we further develop an image encryption algorithm based on 2D-MHM and compressed sensing (CS). Several chaotic metrics verify the randomness and validity of 2D-MHM. These metrics include bifurcation diagram, Lyapunov exponent, initial value sensitivity, 0–1 test, and NIST test. Specifically, CS significantly reduces the ciphertext image size thereby reducing its resource consumption during transmission. Reality-preserving fractional DCT (RP-Fdct) diffusion is utilized to transform pixels into the frequency domain to enhance the encryption effect. Subsequently, lightweight index confusion and XOR diffusion further improve the algorithm security. The security of the algorithm is verified through various experiments. It is able to encrypt grayscale and color images of different sizes with good results. Notably, this algorithm also implements the encryption requirements for binary images. Due to our designs, it outperforms recently reported encryption algorithms in several areas, especially in reconstruction performance.

Entropy Stacked Autoencoder based Diffusion Model (ESADM) and Fuzzy Clustering with Semi Supervised Fuzzy Graph Convolutional Network (FC-SSFGCN) for Rail Surface Defect Detection

Rails, fasteners, and other parts of railway track lines eventually develop flaws due to continuous strain from train operations and direct exposure to the environment; these faults directly affect the safety of train operations. Detecting defects on rail surfaces presents a formidable challenge due to the wide array of possible flaws and their unpredictable nature. However, Defect identification errors, massive variances, inadequate training sample availability, and weak contrast between faults and the surrounding background all contribute to the complexity of this procedure. In this work, rail surface and fastener flaws may be detected non-destructively using a multi-crack detection approach based on the Entropy Stacked Autoencoder Diffusion Model (ESADM). A novel approach, ESAFM, has been developed, combining a rail surface image encoder with a multi-layer, Stacked Autoencoder to extract latent materials from images showcasing different types of cracks. This integration reduces the need for significant processing resources by integrating easily into the conventional physically-based image workflow. Additionally, the Zero Shot-Semi Supervised Fuzzy Class Knowledge Graph (ZS-SSFCKG) method proposes Class Knowledge Graph Construction (CKGC), which constructs a CKG to elucidate the connection between defects and non-defects. Class features are learned by using a Fuzzy Clustering with Semi Supervised Fuzzy Graph Convolutional Network (FC-SSFGCN). The method employs a transformer encoder to capture distant relationships, allowing for the extraction of features from defect samples. This facilitates the acquisition of distinct defect image characteristics, as industrial defects vary in shape and size. The experimental results on the public Railway Track Fault Detection (RTFD)and Rail Surface Defect Datasets (RSDDs) with rail surface defects are collected from rail tracks surface defect detection.