scholarly journals A Security-Enhanced Image Communication Scheme Using Cellular Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1000
Author(s):  
Heping Wen ◽  
Jiajun Xu ◽  
Yunlong Liao ◽  
Ruiting Chen ◽  
Danze Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Cellular Neural Network ◽  
Development Trend ◽  
Security Technology ◽  
Pseudorandom Sequences ◽  
Image Communication ◽  
Image Cipher ◽  
Plain Image ◽  
Communication Scheme ◽  
Data Environment

In the current network and big data environment, the secure transmission of digital images is facing huge challenges. The use of some methodologies in artificial intelligence to enhance its security is extremely cutting-edge and also a development trend. To this end, this paper proposes a security-enhanced image communication scheme based on cellular neural network (CNN) under cryptanalysis. First, the complex characteristics of CNN are used to create pseudorandom sequences for image encryption. Then, a plain image is sequentially confused, permuted and diffused to get the cipher image by these CNN-based sequences. Based on cryptanalysis theory, a security-enhanced algorithm structure and relevant steps are detailed. Theoretical analysis and experimental results both demonstrate its safety performance. Moreover, the structure of image cipher can effectively resist various common attacks in cryptography. Therefore, the image communication scheme based on CNN proposed in this paper is a competitive security technology method.

A GENERALIZED SYNCHRONIZATION THEOREM FOR AN ARRAY OF DIFFERENTIAL EQUATIONS WITH APPLICATION TO SECURE COMMUNICATION

2005 ◽  
Vol 15 (01) ◽  
pp. 119-135 ◽  
Author(s):  
LEQUAN MIN ◽  
XIAODAN ZHANG ◽  
GUANRONG CHEN
Keyword(s):  
Differential Equations ◽  
Secure Communication ◽  
Reaction Diffusion ◽  
Cellular Neural Network ◽  
Two Dimensional ◽  
Generalized Synchronization ◽  
Image Transformation ◽  
Image Communication ◽  
Driven System ◽  
Communication Scheme

This paper first presents a theorem for constructing a unidirectionally coupled array of differential equations (ADE) such that a driving system and a driven system achieve generalized synchronization (GS) via a set of invertible C1-diffeomorphism transformations. Secondly, a new secure communication scheme for digital image transformation is developed based on the theorem. Finally, a Chua's ADE with GS property is introduced, based on a prototype introduced by Chua as a two-dimensional dual-layer two-dimensional reaction–diffusion cellular neural network (CNN), and is applied to construct a secure image communication scheme.

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator

Cellular Neural Network Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization for Noise Removal of Color Image

2011 ◽  
Vol 422 ◽  
pp. 771-774
Author(s):  
Te Jen Su ◽  
Jui Chuan Cheng ◽  
Yu Jen Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Linear Matrix Inequality ◽  
Color Image ◽  
Particle Swarm ◽  
Cellular Neural Network ◽  
Noise Removal ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Swarm Optimization

This paper presents a color image noise removal technique that employs a cellular neural network (CNN) based on hybrid linear matrix inequality (LMI) and particle swarm optimization (PSO). For designing templates of CNN, the Lyapunov stability theorem is applied to derive the criterion for the uniqueness and global asymptotic stability of the CNN’s equilibrium point. The template design is characterized as a standard LMI problem, and the parameters of templates are optimized by PSO. The input templates are obtained by employing the CNN’s property of saturation nonlinearity, which can be used to eliminate noise from arbitrary corrupted images. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed LMI-PSO-CNN methodology.

Sigma–delta cellular neural network for 2D modulation

2008 ◽  
Vol 21 (2-3) ◽  
pp. 349-357 ◽  
Author(s):  
Hisashi Aomori ◽  
Tsuyoshi Otake ◽  
Nobuaki Takahashi ◽  
Mamoru Tanaka
Keyword(s):  
Neural Network ◽  
Cellular Neural Network ◽  
Sigma Delta

CELLULAR NEURAL NETWORKS: ASYMMETRIC SPACE-DEPENDENT TEMPLATES, MOSAIC PATTERNS, AND SPATIAL CHAOS

2004 ◽  
Vol 14 (08) ◽  
pp. 2655-2665 ◽  
Author(s):  
LARRY TURYN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hexagonal Lattice ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Integer Lattice ◽  
Spatial Entropy ◽  
Spatial Chaos

We consider a Cellular Neural Network (CNN), with a bias term, on the integer lattice ℤ2in the plane ℝ2. Space-dependent, asymmetric couplings (templates) appropriate for CNN in the hexagonal lattice on ℝ2are studied. We characterize the mosaic patterns and study their spatial entropy. It appears that for this problem, asymmetry of the template has a more robust effect on the spatial entropy than does the sign of a parameter in the templates.

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