Hybrid chaotic system-oriented artificial fish swarm neural network for image encryption

2021 ◽  
Author(s):  
Junjun Liu ◽  
Jun Zhang ◽  
Shoulin Yin
Keyword(s):  
Neural Network ◽  
Image Encryption ◽  
Chaotic System ◽  
Artificial Fish Swarm

scholarly journals A Novel Plaintext-Related Color Image Encryption Scheme Based on Cellular Neural Network and Chen’s Chaotic System

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 393
Author(s):  
Renxiu Zhang ◽  
Longfei Yu ◽  
Donghua Jiang ◽  
Wei Ding ◽  
Jian Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Encryption ◽  
Chaotic System ◽  
Color Image ◽  
State Of The Art ◽  
Cellular Neural Network ◽  
Switching Function ◽  
Encryption Scheme ◽  
Color Image Encryption ◽  
Extensive Performance

To address the problem that traditional stream ciphers are not sensitive to changes in the plaintext, a novel plaintext-related color image encryption scheme is proposed in this paper, which combines the 6-dimensional cellular neural network (CNN) and Chen’s chaotic system. This encryption scheme belongs to symmetric cryptography. In the proposed scheme, the initial key and switching function generated by the plaintext image are first utilized to control the CNN to complete the scrambling process. Then, Chen’s chaotic system is used to diffuse the scrambled image for realizing higher security. Finally, extensive performance evaluation is undertaken to validate the proposed scheme’s ability to offer the necessary security. Furthermore, the scheme is compared alongside state-of-the-art algorithms to establish its efficiency.

scholarly journals Complex dynamical behaviors of a novel exponential hyper–chaotic system and its application in fast synchronization and color image encryption

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042110033
Author(s):  
Javad Mostafaee ◽  
Saleh Mobayen ◽  
Behrouz Vaseghi ◽  
Mohammad Vahedi ◽  
Afef Fekih
Keyword(s):  
Image Encryption ◽  
Chaotic System ◽  
Sliding Mode ◽  
Color Image ◽  
Equilibrium Points ◽  
Lyapunov Stability Theory ◽  
System Stability ◽  
Color Image Encryption ◽  
Terminal Sliding Mode ◽  
Finite Time Stability

This paper proposes a novel exponential hyper–chaotic system with complex dynamic behaviors. It also analyzes the chaotic attractor, bifurcation diagram, equilibrium points, Poincare map, Kaplan–Yorke dimension, and Lyapunov exponent behaviors. A fast terminal sliding mode control scheme is then designed to ensure the fast synchronization and stability of the new exponential hyper–chaotic system. Stability analysis was performed using the Lyapunov stability theory. One of the main features of the proposed controller is the finite time stability of the terminal sliding surface designed with high–order power function of error and derivative of error. The approach was implemented for image cryptosystem. Color image encryption was carried out to confirm the performance of the new hyper–chaotic system. For image encryption, the DNA encryption-based RGB algorithm was used. Performance assessment of the proposed approach confirmed the ability of the proposed hyper–chaotic system to increase the security of image encryption.

scholarly journals A new image encryption algorithm based on the OF-LSTMS and chaotic sequences

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi He ◽  
Ying-Qian Zhang ◽  
Xin He ◽  
Xing-Yuan Wang
Keyword(s):  
Image Encryption ◽  
Fractional Order ◽  
Chaotic System ◽  
Short Term Memory ◽  
Encryption Algorithm ◽  
Chaotic Sequences ◽  
Long Short Term Memory ◽  
Input Gate ◽  
And Diffusion ◽  
Image Encryption Algorithm

AbstractIn this paper, a novel image encryption algorithm based on the Once Forward Long Short Term Memory Structure (OF-LSTMS) and the Two-Dimensional Coupled Map Lattice (2DCML) fractional-order chaotic system is proposed. The original image is divided into several image blocks, each of which is input into the OF-LSTMS as a pixel sub-sequence. According to the chaotic sequences generated by the 2DCML fractional-order chaotic system, the parameters of the input gate, output gate and memory unit of the OF-LSTMS are initialized, and the pixel positions are changed at the same time of changing the pixel values, achieving the synchronization of permutation and diffusion operations, which greatly improves the efficiency of image encryption and reduces the time consumption. In addition the 2DCML fractional-order chaotic system has better chaotic ergodicity and the values of chaotic sequences are larger than the traditional chaotic system. Therefore, it is very suitable to image encryption. Many simulation results show that the proposed scheme has higher security and efficiency comparing with previous schemes.

Multiple-image encryption based on cascaded gyrator transforms and high-dimensional chaotic system

2021 ◽  
Author(s):  
Xiaoni Sun ◽  
Zhuhong Shao ◽  
Yuanyuan Shang ◽  
Mingxian Liang ◽  
Fengjian Yang
Keyword(s):  
Image Encryption ◽  
Chaotic System ◽  
High Dimensional ◽  
Multiple Image

scholarly journals Hybrid Image Encryption Technique Using Genetic Algorithm and Lorenz Chaotic System

2020 ◽  
Vol 32 ◽  
pp. 03009
Author(s):  
Vishwanath Chikkareddi ◽  
Anurag Ghosh ◽  
Preksha Jagtap ◽  
Sahil Joshi ◽  
Jeel Kanzaria
Keyword(s):  
Genetic Algorithm ◽  
Image Encryption ◽  
Chaotic System ◽  
Random Number ◽  
Current Image ◽  
Space Efficiency ◽  
Highly Sensitive ◽  
Key Sensitivity ◽  
Lorenz Chaotic System ◽  
Time And Space Complexity

One of the important application of image encryption is storing confidential and important images on a local device or a database in such a way that only the authorized party can view or perceive it. The current image encryption technique employs the genetic algorithm to increase confusion in the image, but compromises in time and space complexity. The other method employs chaos or pseudo random number generating systems which have fast and highly sensitive keys but fails to make the image sufficiently noisy and is risky due to its deterministic nature. We propose a technique which employs the non-deterministic, optimizing power of genetic algorithm and the space efficiency and key sensitivity of chaotic systems into a unified, efficient algorithm which will retain the merits of both the methods whereas tries to minimize their demerits in a software system. The encryption process proceeds in two steps, generating two keys. First, an encryption sequence is generated using Lorenz Chaotic system of differential equation. The seed values used are the user’s actual key having key sensitivity of 10-14. Second, the encrypted image’s genetic encryption sequence is generated which will result in an encrypted image with entropy value greater than 7.999 thus ensuring the image is very noisy. Proposed technique uses variations of Lorenz system seed sets to generate all random mutations and candidate solutions in Genetic encryption. Since only the seed sets leading to desired solution is stored, space efficiency is higher compared to storing the entire sequences. Using this image encryption technique we will ensure that the images are hidden securely under two layers of security, one chaotic and other non-deterministic.

Color image encryption based on hybrid hyper-chaotic system and cellular automata

2017 ◽  
Vol 90 ◽  
pp. 225-237 ◽  
Author(s):  
Abolfazl Yaghouti Niyat ◽  
Mohammad Hossein Moattar ◽  
Masood Niazi Torshiz
Keyword(s):  
Cellular Automata ◽  
Image Encryption ◽  
Chaotic System ◽  
Color Image ◽  
Color Image Encryption

scholarly journals A Two-Stage Exon Recognition Model Based on Synergetic Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhehuang Huang ◽  
Yidong Chen
Keyword(s):  
Neural Network ◽  
Digital Signal ◽  
Two Stage ◽  
Recognition Model ◽  
Artificial Fish Swarm Algorithm ◽  
Proposed Model ◽  
Artificial Fish Swarm ◽  
Recognition Efficiency ◽  
Processing Techniques ◽  
Signal Processing Techniques

Exon recognition is a fundamental task in bioinformatics to identify the exons of DNA sequence. Currently, exon recognition algorithms based on digital signal processing techniques have been widely used. Unfortunately, these methods require many calculations, resulting in low recognition efficiency. In order to overcome this limitation, a two-stage exon recognition model is proposed and implemented in this paper. There are three main works. Firstly, we use synergetic neural network to rapidly determine initial exon intervals. Secondly, adaptive sliding window is used to accurately discriminate the final exon intervals. Finally, parameter optimization based on artificial fish swarm algorithm is used to determine different species thresholds and corresponding adjustment parameters of adaptive windows. Experimental results show that the proposed model has better performance for exon recognition and provides a practical solution and a promising future for other recognition tasks.

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