Image Decryption Research Articles

Efficient algorithms for perturbed symmetrical Toeplitz-plus-Hankel systems

This paper investigates a specific class of perturbed Toeplitz-plus-Hankel matrices. We introduce two novel algorithms designed for perturbed symmetrical centrosymmetrical Toeplitz-plus-Hankel systems, offering reduced computational time. Furthermore, we present applications of image encryption and decryption based on these algorithms. Through numerical experiments, we demonstrate the effectiveness of our proposed algorithms.

Secure authentication and encryption via diffraction imaging-based encoding and vector decomposition

Abstract Traditional optical encryption systems have security risks due to their linearity and usually encounter problems such as the heavy burden of key transmission and storage. This paper proposes a novel security-enhanced optical image authentication and encryption framework that combines diffractive imaging-based encryption with the vector decomposition algorithm (VDA). Chaotic random phase masks (CRPMs) are used to encrypt data for authentication via VDA, and a pair of complementary binary matrix keys are utilized to extract information from the encrypted data to generate ciphertext. During the authentication and decryption processes, a sparse reference image is reconstructed from the ciphertext for verification. If the authentication is successful, image decryption can be executed using a key-assisted phase retrieval algorithm. The employment of nonlinear VDA, an additional layer of authentication, and the use of CRPMs and binary matrix keys enhance security and address key burden concerns. Simulation results demonstrate the feasibility, effectiveness, and security of the scheme.

A multidimensional image encryption and decryption technology

This paper investigates various current information encryption technologies that are growing quickly, especially different algorithms for encrypting and decrypting unique patterns in image data. The fast Fourier transform (FFT) has emerged as a powerful tool with the potential to significantly impact encryption technology. Current encryption technology is designed to encrypt and decrypt images and is easy to decipher. Hence, this paper presents the proposed encryption and decryption algorithm to improve encryption technology based on an improved Euler integral and FFT; in particular, the algorithm possesses a more complex and multidimensional way to improve encryption technology. The improved algorithm offers the ability to efficiently process and analyze vast amounts of image data. The proposed approach of matching the proposed encryption and decryption algorithms can be applied to cloud computing and quantum computing technology.

Quantum-based PRNG for image encryption: a comprehensive study

Quantum computing, grounded in the principles of quantum mechanics, has ushered in a new era of intriguing possibilities and fresh scientific insights. In this document, we present an innovative algorithm tailored for the encryption and decryption of images. This algorithm harnesses the quasi-probability values extracted from each state within the quantum system, represented as | ψ 〉 . These numerical values are seamlessly integrated with conventional data to facilitate image encryption and decryption processes. Our methodology undergoes comprehensive validation and evaluation through extensive simulations performed on the IBM Qiskit Aer simulator. We subject the generated numerical sequence to rigorous statistical scrutiny using the NIST SP 800-22 suite, ensuring its authenticity and reliability. Employing a quantum system comprising 3 qubits and uniform rotation gates, our algorithm produces a dataset consisting of 41,943,040 binary digits. Additionally, we conduct a thorough assessment encompassing both visual and statistical analyses to ascertain the effectiveness and robustness of our proposed algorithm. Furthermore, we emphasize the critical importance of achieving a quasi-probability error rate of 10 − 6 for real-world implementation, highlighting the practical viability and scalability of our approach. Through comparative analysis and meticulous examination of correlation patterns, our study provides invaluable insights into the distinctive characteristics and efficacy of our encryption scheme.

Finite-time synchronization for a fully complex-valued BAM inertial neural network with proportional delays via non-reduced order and non-separation approach

This paper focuses on finite-time synchronization (FinTS) for fully complex-valued bidirectional associative memory inertial neural networks (FCV-BAM-INNs) with proportional delays via non-reduced order and non-separation approach. Firstly, a model of the FCV-BAM-INNs with proportional delays is established. Secondly, by using the non-reduced order transformation method of inertial neural networks (INNs) and the non-separation method of complex-valued neural networks (CVNNs), an adjustable complex feedback control law with memory is designed to reach the FinTS of the drive–response FCV-BAM-INNs with proportional delays. Thirdly, some new delay-independent algebraic sufficient criteria are formed to guarantee FinTS of FCV-BAM-INNs with proportional delays by applying non smooth theory and inequalities in the complex domain, which are flexible and adjustable and easy to implement in applications. Moreover, the settling time of FinTS theoretically is calculated. Finally, some numerical simulations and an application on image encryption and decryption as evidences of the validity and availability of the results.

Global quaternion generalized minimal residual method for generalized coupled Sylvester quaternion matrix equations with application to colour image encryption and decryption

ABSTRACT Sylvester matrix equations play important roles in control and system theory. We consider a class of the generalized coupled Sylvester quaternion matrix equations, which includes a lot of special matrix equations such as Stein matrix equation, Lyapunov matrix equation and Sylvester matrix equation on the quaternion skew field. First, the quaternion Krylov subspace and its F-orthogonal basis are constructed via a new quaternion matrix product and the global quaternion Arnoldi process. Then a global quaternion generalized minimal residual (Gl-QGMRES) method is proposed for solving the generalized coupled Sylvester quaternion matrix equations. The convergence of the proposed method is theoretically analysed. Finally, the effectiveness and feasibility of the proposal are verified by some numerical experiments. As an application of Gl-QGMRES method, we establish a kind of colour image encryption and decryption by embedding watermark images.

High-fidelity decryption technology for visual cryptography based on incoherent optical polarization XNOR operation

In this paper, we propose an optical high-fidelity decryption technology for Visual Cryptography (VC) based on incoherent optical polarization exclusive NOR (XNOR) operation. The plaintext image is divided into two binary ciphertexts by the Random-Grid-based Visual Secret Sharing (RGVSS) algorithm. These ciphertexts are printed onto the polarizing films with orthogonal polarization. The P- and S-polarizing pixels in the polarizing films represent the “black” and “white” pixels of the ciphertexts, respectively. Due to the optical transmission property of polarizing film, the pixel in the decrypted image appears bright only when the stacked pixels for the two ciphertexts have the same polarizing orientation. High-fidelity image decryption is then achieved. The proposed technology has been successfully demonstrated through simulation and experimentation. Thanks to its advantages of no pixel expansion, incoherent illumination, being free of computer assistance, and flexibility of ciphertext carriers, this method provides a broad prospect for convenient, large-format, high-fidelity information recovery applications for VC.

Investigation of the complex dynamical structure of bifurcation and dark soliton solutions to fractional generalized double [formula omitted]-Gordon equation

In the current research work, the classical wave equation is combined with a nonlinear sinh source term in the sinh-Gordon equation. It has been used in several scientific domains such as differential geometry theory, integrable quantum field theory, kink dynamics, and statistical mechanics. It makes more comprehensible the dynamics of strings and multi-strings in the constant curvature space. The current study has three main objectives. Examine the governing model to get novel solutions by employing the modified Kudryashov technique. Then compare it with the numerical technique of modified variational iteration method (MVIM) to calculate the error terms. Furthermore, employing bifurcation theory to produce a dynamical system. Additionally, use the dynamical system’s sensitivity analysis to investigate the model’s sensitivity. At last, for the validation of acquired results, the cryptography technique of novel image encryption and decryption is used. The research is greatly enhanced by the presentation of thorough 2D and 3D phase portraits. The field of mathematics and other sciences will benefit from these discoveries.

Enhanced Semantic Visual Cryptography with AI-driven error reduction for improved two-dimensional image quality and security

Abstract Visual Cryptography (VC) has emerged as a vital technique in the information security domain, with the fundamental purpose of securing 2-Dimensional (2D) image content through encryption and facilitating secure communication. While traditional VC has been instrumental in safeguarding data, it often falls short in maintaining image quality and semantic accuracy upon reconstruction. To address these limitations, this research encompasses the development of an Enhanced Semantic VC (ESVC) model, which aims to refine the encryption process while ensuring the semantic integrity of the images. The ESVC model introduces a new approach that merges VC with artificial intelligence (AI) to enhance 2D image encryption and decryption. The novel aspect of this research lies in the integration of AI-driven reinforcement learning (RL) to increase the quality of the 2D image by measuring the errors between the original secret image and the reconstructed image. This innovative framework is tailored for the secure transmission of 2D grayscale images, ensuring the preservation of semantic integrity while measuring and minimizing image quality loss. By integrating RL algorithms with a measurement of error reduction protocol, the model promises robust encryption capabilities with enhanced resilience against a plethora of cyber threats, thereby elevating the standard for secure image communication. Empirical evaluation of the ESVC model yields promising results, with the Peak Signal-to-Noise Ratio (PSNR) of reconstructed images achieving impressive values between +39 and +42 decibels (dB). These findings underscore the ESVC model’s capability to produce high-fidelity decrypted images, significantly surpassing traditional VC methods in both security and image quality. The research findings illuminate the potential of merging AI with VC to achieve a harmonious balance between computational efficiency and encryption strength, marking a significant advancement in the domain of visual data protection.

A Universal Image Compression Sensing–Encryption Algorithm Based on DNA-Triploid Mutation

With the fast growth of information technology (IT), the safety of image transmission and the storing of images are becoming increasingly important. Traditional image encryption algorithms have certain limitations in transmission and security, so there is an urgent need for a secure and reliable image encryption algorithm. A universal compression sensing (CS) image encryption (IE) algorithm based on DNA-triploid mutation (DTM) is presented in this paper. Firstly, by using the CS algorithm, an image is compressed while obtaining a range of chaotic sequences by iteration of a chaotic map. Then, DNA sequences are generated by encoding the image and, based on the DTM, new mutant DNA sequences are generated according to specific rules. Next, the chaotic sequences are operated at the DNA level to perform confusion and diffusion operations on the image to ensure the security of the data. Finally, DNA decoding is carried out to obtain the compressed encrypted image. The simulation results show that the algorithm can effectively complete encryption and decryption of images. The performance test results show that the algorithm has a sufficiently large key space of 10587. The information entropy of the cipher image is close to 8. In summary, both simulation experiments and performance tests fully show that a high level of security and reliability for the proposed algorithm in protecting image privacy is achieved.

Deep-KEDI: Deep learning-based zigzag generative adversarial network for encryption and decryption of medical images.

Medical imaging techniques have improved to the point where security has become a basic requirement for all applications to ensure data security and data transmission over the internet. However, clinical images hold personal and sensitive data related to the patients and their disclosure has a negative impact on their right to privacy as well as legal ramifications for hospitals. In this research, a novel deep learning-based key generation network (Deep-KEDI) is designed to produce the secure key used for decrypting and encrypting medical images. Initially, medical images are pre-processed by adding the speckle noise using discrete ripplet transform before encryption and are removed after decryption for more security. In the Deep-KEDI model, the zigzag generative adversarial network (ZZ-GAN) is used as the learning network to generate the secret key. The proposed ZZ-GAN is used for secure encryption by generating three different zigzag patterns (vertical, horizontal, diagonal) of encrypted images with its key. The zigzag cipher uses an XOR operation in both encryption and decryption using the proposed ZZ-GAN. Encrypting the original image requires a secret key generated during encryption. After identification, the encrypted image is decrypted using the generated key to reverse the encryption process. Finally, speckle noise is removed from the encrypted image in order to reconstruct the original image. According to the experiments, the Deep-KEDI model generates secret keys with an information entropy of 7.45 that is particularly suitable for securing medical images.

Securing Images using Bifid Cipher associated with Arnold Map

In this paper, a scheme for image data security is designed by using Bifid cipher and Arnold map. The conventional 2D-Bifid cipher is changed to handle the encryption and decryption of color images. Further, a block-based method is realized using Arnold map to encrypt and decrypt the image data of square as well as non-square sizes. The proposed scheme is constructed on a widely adopted cryptographic framework, i.e., substitution-permutation design, where Bifid cipher-based scheme acts as a substitution layer and Arnold map-based scheme acts as a diffusion layer. The designed scheme has a huge key space with key sensitivity not only to accurate keys but also to their accurate orders. The practicability and performance of the designed scheme is validated by conducting a detailed analysis along with a comparative study. This paper contributes to a simple, efficient and secure image encryption method, which outperforms to the related works.

Deep learning decryption approach for asymmetric computer-generated holography (CGH) cryptosystem

Deep-learning-based optical image decryption has attracted attention due to its remarkable advantages of keyless managements. Here, a high-fidelity deep learning (DL) decryption strategy is proposed, aiming for the asymmetric DRPE-based CGH cryptosystem, which is combined with phase truncation technique and chaotic iris phase masks. First, a mass of ciphertext and plaintext image pairs are generated to create a dataset. Then, a deep neural network, namely ACGHC-Net (network for the asymmetric DRPE-based CGH cryptosystem), is designed and trained in a supervised learning manner. After the model training and tuning, the ACGHC-Net can quickly and accurately decrypt the ciphertext images. The average cross-correlation coefficient (CC) of the decrypted images achieves 0.998, the average structural similarity (SSIM) 0.895, and the average peak signal-to-noise ratio (PSNR) 31.090 dB. Furthermore, we conducted anti-noise and anti-clipping analysis on the ACGHC-Net. The results prove that the proposed ACGHC-Net can successfully decrypt the encrypted complex grayscale images, and has good anti-noise and anti-cropping robustness for the asymmetric DRPE-based CGH cryptosystem. The proposed method will be expected to further boost keyless decryption in image encryption systems.

A color image encryption and decryption scheme based on extended DNA coding and fractional-order 5D hyper-chaotic system

With the advancement of information technology, the security of digital images has become increasingly important. To ensure the integrity of images, this paper presents a novel color image encryption and decryption architecture. Firstly, a fractional-order five-dimensional hyper-chaotic system (F5DHS) is proposed and used to generate chaotic sequences for permutation and diffusion processes. Secondly, the extended DNA encoding scheme is employed to generate more DNA encoding rules and four DNA computation methods are used to improve the security of the encryption scheme. Thirdly, a blocked image encryption method is designed to divide the color image into sub-blocks and then determine the DNA encoding, DNA decoding and DNA arithmetic rules for each sub-block based on chaotic sequences. Finally, the results of simulation experiments and security analysis show that the color image encryption algorithm has good encryption performance, a large secret key space, and strong robustness to noise and data loss attacks, indicating that the proposed encryption algorithm can effectively protect the secure communication of digital images.

Controllable Electrical Properties of ZrO2/BiFeO3 Bilayer Memristor from Synaptic Mimic to TRNG Circuit Application by Modulating Compliance Currents

AbstractApart from simulating biological synapses, memristors can also be used in the secure encryption by exploiting their inherent random resistive switching (RS) properties. In this work, nonvolatile Ta/BiFeO3/ZrO2/Pt memristor is fabricated with 2 nm inserting BiFeO3 layer. At a high compliance current of 10 mA, it presents gradual RS characteristics during the set process, which resulting in 30 conductance states. Synaptic behavior can be successfully mimicked by precise conductance modulating under pulse electrical stimulation. Whereas under a low compliance current of 500 µA, it exhibits abrupt RS behavior, and the set voltage ranged from 0.3–0.8 V, which can be effectively as an entropy source for true random number generator (TRNG). Due to its Shannon entropy of 300 bits is 0.99987, Hamming weights and intra‐Hamming distances tend to be 50%. Furthermore, the software simulation of encryption and decryption of cat image is achieved by combining the key generated by TRNG. The controllable electrical properties of the Ta/BiFeO3/ZrO2/Pt device can be modulated by the compliance current, thus meeting the computing or safety requirements, which provides a solid foundation for the development of integrating computation and security at the hardware level.

Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption’s effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm’s capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm’s efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics.

Synchronization of fractional-order quaternion-valued neural networks with image encryption via event-triggered impulsive control

The finite-time synchronization and impulsive synchronization of fractional-order quaternion-valued neural networks (FOQVNNs) with delay are investigated in this research. To begin, instead of employing a decomposition method, a Lyapunov direct approach is proposed to achieve the finite-time synchronization of FOQVNNs using event-triggered control based on state errors. The settling time is then estimated using a unique fractional differential inequality. The event-triggered impulsive controller, which updates only during triggering instants, is proposed to be more inexpensive and efficient. Then, utilizing fractional Lyapunov theory and the comparison principle, several novel quaternion-valued linear matrix inequalities criteria are defined to assure the impulsive synchronization of the delayed FOQVNNs. Furthermore, the positive lower bound of the inter-event time is obtained to exclude the Zeno behavior. As proof of the feasibility and validity of these theoretical findings obtained, numerical examples are provided. At last, the use of the event-triggered control technique for FOQVNNs in image encryption and decryption is demonstrated and validated.

Image cryptosystem with image key using integral disproportion

The subject matter of this article is the process of securing visual data through encryption. The goal of this study is to develop a new approach for image encryption and decryption using an image as a key, where security and performance are comparable or superior to alternatives. The tasks to be solved are as follows: developing a symmetric image cryptosystem for both greyscale and colored images in any format that can use an arbitrary image as a security key; adapting the capabilities of integral disproportionality for image encryption. The encryption method applies an integral disproportion formula for each pair of pixels’ components for both the input image and the key image starting from the first one. The output is a ciphered object represented as an array of real numbers, with decryption being the inverse process. The result is a novel approach to image encryption, proposing a new cryptosystem in which an arbitrary image of varying size, format, or content serves as the encryption key. The proposed symmetric full-encryption cryptosystem is applicable to both greyscale and colored images in any format. The method employs a frequency-based approach that influences the values of pixels rather than their positions. In contrast to mainstream approaches in this area, the representation of a cipher is not an image but a binary array. The proposed algorithm is simple to implement. The proposed method appears to meet the required requirements in both security and performance. Conclusions. The scientific novelty of the results obtained is as follows: This research introduces a new image encryption method, which is unique in its application of integral disproportionality to alter pixel values, differing from traditional encryption methods. This provides a practical yet secure option to the existing encryption techniques.

Colour Image Encryption and Decryption using Arnold’s Cat Map and Henon Map

Image encryption plays a crucial role in securing sensitive visual information during transmission and storage. Throughout the encryption and decryption phases of images, the utilized ciphers exhibit substandard quality, resulting in the persistence of the original image. Consequently, the issues at hand stem from a deficiency in the quality of cipher image, thereby compromising the confidentiality level to an unsafe extent. This research explores the application of chaotic maps, specifically the Arnold Cat Map and Henon Map for the encryption and decryption of digital colour images. The encryption process involves applying the Arnold Cat Map and Henon Map to permute and diffuse pixel values, enhancing the confusion and diffusion properties essential for robust encryption. The study provides insights into the decryption efficiency and the ability to recover the original image from the ciphered version. This research contributes to the body of knowledge in colour image encryption techniques and offers a novel approach leveraging chaotic maps. The findings emphasize the importance of selecting suitable chaotic maps and their parameters to achieve a balance between security and image quality.

Image security using steganography and cryptography with sweeping computational ghost imaging

A sweeping computational ghost imaging (SCGI)-based encryption system is intended for increased data security and speedier data transport. SCGI is combined with steganography and cryptography processes in this system. SCGI requires fewer shots, resulting in faster image capture, transmission, encryption, and decryption. This strategy also results in smaller, more compact data packages and higher bitrates. Least significant bit (LSB) uses steganography to conceal the hidden picture. In the case of Rivest–Shamir–Adleman (RSA) encryption, public and private keys are generated via a one-way function based on bucket values. Encryption is performed on two levels, with an asymmetric approach divided into three sub-items that significantly increase encryption. Surprisingly, the method uses fewer pictures for image reconstruction, resulting in faster image reconstruction. SCGI promises applications in an extensive number of data encryption sectors since this technology leads to smaller data packages and higher bitrates. The presented approach is examined using the number of pixel change rate (NPCR), normalized root mean square (NRMS), peak signal-to-noise ratio (PSNR), and correlation coefficient (CC), which indicates constant encryption improvement. We experimentally and situationally demonstrate our findings under eavesdropping, which prove the resistance and robustness of our methods. In optimal settings, this innovation enhances encryption by up to 90% when compared to traditional encryption methods.