Application In Image Encryption Research Articles

Complex hidden dynamics in a memristive map with delta connection and its application in image encryption

Complex hidden dynamics in a memristive map with delta connection and its application in image encryption

Dynamic behavior of small system under the influence of parameters and application of image encryption

Dynamic behavior of small system under the influence of parameters and application of image encryption

Fixed/predefined-time projective synchronization for a class of fuzzy inertial discontinuous neural networks with distributed delays

In this article, fixed-time projective synchronization (FTPS) and predefined-time projective synchronization (PTPS) for a class of discontinuous fuzzy inertial neural networks (FINNs) with distributed delays are considered. To this end, regarding differential inclusion theories and the Lyapunov stability method, more precise and improved setting times of FTPS are systematically constructed. Furthermore, a more general and novel predefined-time stability lemma is established to make the synchronized time more flexible and can be an accurate value in advance. Meanwhile, some effective feedback controllers and new criteria are established to guarantee the proposed FINNs achieve FTPS and PTPS, respectively. Finally, the corresponding numerical simulations and image encryption application are offered to confirm the obtained results.

A tri-valued memristive chaotic system with hidden attractors and its image encryption application

A tri-valued memristive chaotic system with hidden attractors and its image encryption application

A Memristive Fully Connect Neural Network and Application of Medical Image Encryption Based on Central Diffusion Algorithm

A Memristive Fully Connect Neural Network and Application of Medical Image Encryption Based on Central Diffusion Algorithm

A 5-D memristive hyperchaotic system with extreme multistability and its application in image encryption

By coupling memristors to nonlinear circuits, more complex dynamical behaviors can be induced. However, to date, there has been insufficient attention given to high-dimensional chaotic systems based on memristors. In this paper, a magnetic-controlled memristor is combined with a three-dimensional chaotic system, resulting in a five-dimensional memristive chaotic system. Through dynamic analysis and numerical simulations, the chaotic nature of the system is elucidated based on fundamental system behaviors, including Lyapunov dimension, dissipativity, stability of equilibrium points, 0–1 test, and Poincaré mapping. During the complex dynamical analysis of this system, unique dynamical behaviors are discovered, including intermittent chaos, transient chaos, extreme multistability, and offset-boosting. Moreover, the consistency between numerical calculations and the physical implementation of the actual system is verified through equivalent circuit design. Finally, this system is applied to image encryption, leading to the design of an efficient and secure hyper-chaotic image encryption algorithm, whose effectiveness is confirmed through several security tests.

MEMS-integrated metasurfaces for dynamic linear polarizers

Optical metasurfaces (OMSs), planar arrays of meticulously designed meta-atoms, are renowned for remarkable capabilities in manipulating the polarization state of light at subwavelength scales. Nevertheless, most OMS-empowered polarization optics remain static, featuring well-defined optical responses determined by their configurations set during fabrication. Here, we demonstrate a MEMS-OMS-based dynamic linear polarizer (DLP) with an electrically controlled extinction ratio, which is tunable in a fast and reversible fashion, by combining an anisotropic plasmonic OMS with a thin-film piezoelectric MEMS mirror. Capitalizing on the MEMS-OMS DLP, we further implement voltage-controlled grayscale imaging and vector vortex beam generation under linearly and circularly polarized excitations, respectively. Our MEMS-OMS DLP design could enable adaptive photonic systems for advanced applications in optical image encryption, displays, and beyond.

Dynamic S-Box Construction Using Mordell Elliptic Curves over Galois Field and Its Applications in Image Encryption

Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.

Static output feedback synchronization for Markov jump complex dynamical networks with time-varying delay

This paper focuses on the static output feedback synchronization problem of Markov jump complex dynamic networks with time-varying delays. First, a Markov process is introduced to model random mutation phenomena among topologies of complex dynamic networks. Then, a sampling controller is designed to address practical needs. At the same time, some sufficient conditions are provided to enable the system to meet a mixed H∞/passive performance index. Moreover, the article adopted a free weight matrix combined with a novel decoupling method, which removes the equality constraints, the requirement on the rank of the output matrix, and the use of some inequalities. Finally, the effectiveness of the theoretical framework in this paper is demonstrated through a set of numerical examples and their application in image encryption.

An intelligent controller of homo-structured chaotic systems under noisy conditions and applications in image encryption

Due to the maturation of research on chaos and secure communication, the control technology of nonlinear systems, specifically chaos synchronization, has captured the attention of numerous researchers. Focusing on the issues of inflexibility in the design of chaotic synchronization controllers, the need for prior synchronization of the target system structure, and noise's disruptive impact on synchronization, this paper presents solutions that enhance the practical application of chaos. Firstly, the RBF neural controller is adjusted in this paper to bolster the control precision of the chaotic system and enhance its resilience to external disturbances. Secondly, this article presents an enhanced PSO optimization algorithm for the improved RBF neural controller to improve the optimization efficiency of the controller parameters. Finally, the simulation results of the Lorenz system validate the feasibility of the proposed synchronization control scheme. Additionally, the use of chaotic synchronization in image encryption demonstrates that synchronization accuracy can fulfill the requirements of image encryption application scenarios.

Design of multi-scroll chaotic attractor based on a novel multi-segmented memristor and its application in medical image encryption

Medical images contain rich individual health information, making the protection of their privacy and security crucial. This study first proposes a novel multi-segment memristor based on a multi-segment linear function. Then, building upon the Sprott-B chaotic system, a mirror-symmetric memristor multi-scroll chaotic attractor (MMSCAs) is introduced by incorporating logic pulse signals and the novel multi-segment memristor. Dynamic analysis of MMSCAs is conducted from four aspects: equilibrium points, Lyapunov exponents and bifurcations, coexisting attractors, and complexity. Lyapunov exponents and bifurcation diagram analysis reveal rich dynamical behaviors in MMSCAs, including inverse period-doubling bifurcations, burst chaotic, transient chaotic, and offset boosting. MMSCAs exhibit periodic and chaotic attractors co-existing under different initial conditions, along with multi-stability and super multi-stability. Complexity analysis results indicate that MMSCAs possess higher complexity and better randomness compared to other memristor chaotic systems. The accuracy of the MMSCAs mathematical model is verified through circuit design and simulation, and the implementation of MMSCAs in the embedded domain is extended using the STM32 microcontroller. Finally, a new cryptographic system is designed by integrating MMSCAs with RNA computation and applied to medical image encryption. The security of the cryptographic system is evaluated through key space and sensitivity, histogram, and correlation, while its robustness is evaluated through resistance to cropping and noise. The analysis results demonstrate high security and strong robustness of the cryptographic system, offering a novel solution for the protection of medical image information.

Fixed-Time Synchronization for Fractional-Order Cellular Inertial Fuzzy Neural Networks with Mixed Time-Varying Delays

Due to the widespread application of neural networks (NNs), and considering the respective advantages of fractional calculus (FC), inertial neural networks (INNs), cellular neural networks (CNNs), and fuzzy neural networks (FNNs), this paper investigates the fixed-time synchronization (FDTS) issues for a particular category of fractional-order cellular-inertial fuzzy neural networks (FCIFNNs) that involve mixed time-varying delays (MTDs), including both discrete and distributed delays. Firstly, we establish an appropriate transformation variable to reformulate FCIFNNs with MTD into a differential first-order system. Then, utilizing the finite-time stability (FETS) theory and Lyapunov functionals (LFs), we establish some new effective criteria for achieving FDTS of the response system (RS) and drive system (DS). Eventually, we offer two numerical examples to display the effectiveness of our proposed synchronization strategies. Moreover, we also demonstrate the benefits of our approach through an application in image encryption.

Predefined-time sliding mode control for synchronization of uncertain hyperchaotic system and application

The main goal of this paper is to investigate the predefined-time sliding mode control and synchronization of uncertain hyperchaotic systems and its application in secure communication. Firstly, a novel predefined-time sliding surface is designed. Then a predefined-time sliding mode controller is proposed for uncertain hyperchaotic systems, which can guarantee synchronization of the master-slave system within a predefined time in the presence of parameter uncertain and external disturbance. Specially, the settling time, as a part of control parameters, can be predefined as needed independent of initial conditions. Furthermore, the validity of the proposed approach is proved by Lyapunov stability theory. Finally, comparative tests and image encryption application are given to show advantages of the proposed method.

Finite-time synchronization of delayed fuzzy inertial neural networks via intermittent control

This paper addresses the finite-time synchronization (FTS) issue of delayed fuzzy inertial neural networks (DFINNs) via intermittent control. The considered network model takes the inertia term and fuzzy logic into account, which complements some of the existing models. By an appropriate variable substitution, the second-order DFINNs are turned into first-order equation forms. Then, based on the finite-time stability theory, intermittent control strategies are adopted to ensure the FTS of DFINNs and the corresponding criteria are derived thereafter. Besides, the estimation of settling-time for FTS is presented. Finally, the numerical simulations and the image encryption application are presented to illustrate the validity of main results and the control method.

A novel infinitely coexisting attractor and its application in image encryption

A novel infinitely coexisting attractor and its application in image encryption

Hidden extreme multistability in a smooth flux-controlled memristor based four-dimensional chaotic system and its application in image encryption

Abstract This paper introduces and investigates the dynamic analysis of a four-dimensional chaotic system based on a flux-controlled memristor and its application in image encryption. The analysis of equilibrium points of the system shows that it has no equilibrium points and therefore belongs to the family of systems with hidden attractors. It is demonstrated by computational simulations that the system under study experiences rich and complicated dynamics such as coexisting hidden attractors, hidden extreme multistability, offset boosting dynamics and antimonotonicity. A physical circuit that can reproduce the complex behaviors of the system is constructed and realized. The results are in agreement with those from computational simulations. According to the interesting features of the system, it is exploited to generate random bits for image encryption. The robustness and efficiency of the designed image encryption algorithm are tested by providing some statistical tests and security performances. It is found that the designed image encryption algorithm is efficient and robust compared to other image encryption algorithms investigated in some other recent related works.

Employment of Synchronized Chaotic SHA256 Random Number Generators along with their Application in the Field of Image Encryption

Background: Current work is associated with the deployment of synchronized chaotic Secure Hash Algorithm (SHA256) random number generators along with their application in image encryption. Methods: This study aimed to design a perfect communication system to encrypt and decrypt the images via image processing. We introduced a new sliding mode scheme for master-slave fourdimensional (4D) Lorenz-Stenflo systems to deal with the synchronization problem. Then, a new synchronized dynamic secure hash algorithm (SHA-256) was implemented to generate the dynamic random numbers. Results: An image encryption and decryption mechanism was implemented by using synchronized chaotic master-slave systems corresponding to the generated hash values. Here, the master systems were used for encryption, and the slave systems were responsible for the decryption mechanism. The complete design of this paper was implemented in a Python environment. Conclusion: The demonstration of simulation results includes the state responses, strange attractors, and synchronization error responses of master-slave chaotic systems with appropriate switching functions and control input. conclusion: An image encryption and decryption mechanism has been implemented by using synchronized chaotic master-slave systems corresponding to the generated hash values other: The complete design of this paper has been implemented on python environment

Dynamic Analysis of a Four-Wing Chaotic System and Application in Image Encryption Based on Compressive Sensing

Dynamic Analysis of a Four-Wing Chaotic System and Application in Image Encryption Based on Compressive Sensing

Protocol-Based Synchronization of Stochastic Jumping Inertial Neural Networks Under Image Encryption Application.

This work investigates the protocol-based synchronization of inertial neural networks (INNs) with stochastic semi-Markovian jumping parameters and image encryption application. The semi-Markovian jumping process is adopted to characterize INNs under sudden complex changes. To conserve the limited available network bandwidth, an adaptive event-driven protocol (AEDP) is developed in the corresponding semi-Markovian jumping INNs (S-MJINNs), which not only reduces the amount of data transmission but also avoids the Zeno phenomenon. The objective is to construct an adaptive event-driven controller so that the drive and response systems maintain synchronous relationships. Based on the appropriate Lyapunov functional, integral inequality, and free weighting matrix, novel criteria are derived to realize the synchronization. Moreover, the desired adaptive event-driven controller is designed under a semi-Markovian jumping process. The proposed method is demonstrated through a numerical example and an image encryption process.

A non-degenerate chaotic bits XOR system with application in image encryption

The existing chaotic systems exhibit chaos degradation phenomena because of the limited chaotic precision in the real number field. In response to this deficiency, this paper proposes a chaotic bits XOR system (CBXS) in the integer field satisfying Devaney’s chaos. That is, CBXS has the property of sensitivity of initial value, topological transitivity, and periodic point density. Then, a series of chaotic and random test results illustrate that the sequences generated by CBXS with linear feedback shift register (LFSR) in the finite field GF(2) have good random and chaotic performance. It can overcome the chaos degradation problem because it is generated in the integer field and can avoid the limited precision in the real number field. Finally, we employ the proposed CBXS to image encryption with the optimized Arnold’s transformation. The experimental results verify that even after simple encryption, the cipher image still exhibits good encryption effect and efficiency, which illustrates the effectiveness of CBXS.