Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems

In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov’s method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.MINECO/ FEDER under the RTI2018-098913-B100 CV20-45250 and A-TIC- 080-UGR18 project

Impulsive Synchronization and Adaptive-Impulsive Synchronization of a Novel Financial Hyperchaotic System

The impulsive synchronization and adaptive-impulsive synchronization of a novel financial hyperchaotic system are investigated. Based on comparing principle for impulsive functional differential equations, several sufficient conditions for impulsive synchronization are derived, and the upper bounds of impulsive interval for stable synchronization are estimated. Furthermore, a nonlinear adaptive-impulsive control scheme is designed to synchronize the financial system using invariant principle of impulsive dynamical systems. Moreover, corresponding numerical simulations are presented to illustrate the effectiveness and feasibility of the proposed methods

A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption

One of the greatest discoveries of the 20th century was the chaotic phenomenon, which has been a popular area of study up to this point. The Lorenz Attractor is a mathematical model that describes a chaotic system. It is a solution to a set of differential equations known as the Lorenz Equations, which Edward N. Lorenz originally introduced. Hybridizing the Deep Neural Network (DNN) with the K-Means Clustering algorithm will increase the accuracy and reduce the data complexity of the Lorenz dataset. Then, hyperparameters of DNN must be tuned to get the best setting for a given problem, and it becomes crucial to evaluate them to verify whether the model can accurately categorize the data. Furthermore, conventional encryption methods such as Data Encryption Standards (DES) are not adapted to image data because of their high redundancy and big capacity. The first research objective is to develop a new deep learning algorithm by a hybrid of DNN and K-Means Clustering algorithms for estimating the Lorenz chaotic system. Then, this study aims to optimize the hyperparameters of the developed DNN model using the Arithmetic Optimization Algorithm (AOA) and, lastly, to evaluate the performance of the newly proposed deep learning model with Simulated Kalman Filter (SKF) algorithm in solving image encryption application. This work uses a Lorenz dataset from Professor Roberto Barrio of the University of Zaragoza in Spain and focuses on multi-class classification. The dataset was split into training, testing, and validation datasets, comprising 70%, 15%, and 15% of the total. The research starts with developing the hybrid deep learning model consisting of DNN and a K-Means Clustering Algorithm. Then, the developed algorithm is implemented to estimate the parameters of the Lorenz system. In addition, the hyperparameter tuning problem is considered in this research to improve the developed hybrid model by using the AOA algorithm. Lastly, a new hybrid technique suggests tackling the current image encryption application problem by using the estimated parameters of chaotic systems with an optimization algorithm, the SKF algorithm. The fitness function used is the correlation function in the SKF algorithm to optimize the cipher image produced using the Lorenz system. Next, the thesis will be discussed about the findings of this study. As for accuracy, the developed model obtained 72.27% compared to 66.47% for the baseline model. Besides, the baseline model's loss value is 0.3661, while the developed model is 0.1712, lower than the standalone model. Hence, the clustering algorithm is performed well to enhance the accuracy of the model performances, as mentioned in the first objective. The combination of the first two objectives obtained the R2 value of 0.8054 and ρ value of 0.9912, which are higher than the standalone DNN model. Then, for the hybrid model, the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values are 0.1964 and 0.0045, respectively. Both error values are lower than the baseline model, 0.2913 and 0.1976. The findings showed that the model improved the model’s effectiveness and could predict the outcome accurately. This study also discusses the detailed analysis of the developed combined image encryption, including the statistical, security, and robustness analysis related to the third objective. The comparisons between seven image encryption schemes were discussed at the end of the subtopic. Based on the cropping attack’s findings, the proposed technique obtained higher Peak Signal Noise Ratio (PSNR) values for two conditions, which are 1/16 and 1/4 cropping ratios. At the same time, Zhou et al. performed a higher PSNR value for a 1/2 cropping ratio only. In conclusion, hybrid DNN with the K-Means Clustering Algorithm is proven to resolve parameter estimations of the chaotic system by developing an accurate prediction model

Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

State Estimator Design of Generalized Liu Systems with Application to Secure Communication and Its Circuit Realization

The generalized Liu system is firstly introduced and the state observation problem of such a system is explored. A simple state estimator for the generalized Liu system is developed to guarantee the global exponential stability of the resulting error system. Applications of proposed state estimator strategy to chaotic secure communication, circuit implementation, and numerical simulations are provided to show the effectiveness and feasibility of the obtained results. Besides, the guaranteed exponential convergence rate of the proposed state estimator and that of the proposed chaotic secure communication can be precisely calculated

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Synchronization of inertial memristive neural networks with time-varying delays via static or dynamic event-triggered control

Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 61971185, the Major Research Project of the National Natural Science Foundation of China under Grant 91964108 and the Open Fund Project of Key Laboratory in Hunan Universities under Grant 18K010. Publisher Copyright: © 2020 Elsevier B.V.This paper investigates the synchronization problem of inertial memristive neural networks (IMNNs) with time-varying delays via event-triggered control (ETC) scheme and state feedback controller for the first time. First, two types of state feedback controllers are designed; the first type of controller is added to the transformational first-order system, and the second type of controller is added to the original second-order system. Next, based on each feedback controller, static event-triggered control (SETC) condition and dynamic event-triggered control (DETC) condition are presented to significantly reduce the update times of controller and decrease the computing cost. Then, some sufficient conditions are given such that synchronization of IMNNs with time-varying delays can be achieved under ETC schemes. Finally, a numerical simulation and some data analyses are given to verify the validity of the proposed results.Peer reviewe

Stabilizing Unstable Periodic Orbits of the Multi-Scroll Chua's Attractor

This paper addresses the control of the n-scroll Chua’s circuit. It will be shown how chaotic systems with multiple unstable periodic orbits (UPOs) detected in the Poincar´e section can be stabilized as well as taking the system dynamics from one UPO to another