Noise removal using Cohen-Grossberg neural network for improving the quality of the decrypted image in color encryption

In this paper, a color image encryption method is proposed with the removal of noise generated during the transmission based on Cohen-Grossberg neural networks, where the color image is expressed in terms of the standard red-green-blue (RGB) space, and the corresponding pixel matrix is hidden by Arnold transform (AT). The Cohen-Grossberg neural network is added to store the hidden message as the stable equilibria, which achieves the noise removal. The hidden message without noise is recovered by performing AT with accurate iteration numbers. Experimental results show that the proposed method achieves effective resistance against transmission noise.

[1]  Wansheng Tang,et al.  Color image associative memory on a class of Cohen-Grossberg networks , 2010, Pattern Recognit..

[2]  Ting Liu,et al.  Color image encryption by using Arnold transform and color-blend operation in discrete cosine transform domains , 2011 .

[3]  P. Thangavel,et al.  Noise Removal Using Hopfield Neural Network in Message Transmission Systems , 2008, 2008 Second UKSIM European Symposium on Computer Modeling and Simulation.

[4]  Zhengjun Liu,et al.  Color image encryption by using Arnold and discrete fractional random transforms in IHS space , 2010 .

[5]  S. Han,et al.  Fingerprinted secret sharing steganography for robustness against image cropping attacks , 2005, INDIN '05. 2005 3rd IEEE International Conference on Industrial Informatics, 2005..

[6]  Chin-Chen Chang,et al.  A novel digital image watermarking scheme based on the vector quantization technique , 2005, Comput. Secur..

[7]  Zhengjun Liu,et al.  A discrete fractional random transform , 2005, math-ph/0605061.

[8]  A. Murat Tekalp,et al.  Lossless generalized-LSB data embedding , 2005, IEEE Transactions on Image Processing.

[9]  Stephen Grossberg,et al.  Absolute stability of global pattern formation and parallel memory storage by competitive neural networks , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Shi-Jinn Horng,et al.  Novel SCAN-CA-based image security system using SCAN and 2-D von Neumann cellular automata , 2010, Signal Process. Image Commun..

[11]  Madhusudan Joshi,et al.  Color image encryption and decryption using fractional Fourier transform , 2007 .

[12]  Donghui Guo,et al.  A New Symmetric Probabilistic Encryption Scheme Based on Chaotic Attractors of Neural Networks , 2004, Applied Intelligence.

[13]  Zhengjun Liu,et al.  Color image encryption by using the rotation of color vector in Hartley transform domains , 2010 .

[14]  Thomas S. Huang,et al.  An additive approach to transform-domain information hiding and optimum detection structure , 2001, IEEE Trans. Multim..

[15]  D. Artz,et al.  Digital steganography: hiding data within data , 2001 .

[16]  Amir Akhavan,et al.  A Novel Block Cipher Based on Hierarchy of One-Dimensional Composition Chaotic Maps , 2006, 2006 International Conference on Image Processing.

[17]  Mohammed Ghanbari,et al.  High capacity, reversible data hiding in medical images , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[18]  V. I. Arnolʹd,et al.  Ergodic problems of classical mechanics , 1968 .

[19]  Kevin Curran,et al.  An evaluation of image based steganography methods , 2006, Multimedia Tools and Applications.

[20]  Yu-Chen Hu,et al.  Reversible image hiding scheme using predictive coding and histogram shifting , 2009, Signal Process..

[21]  A. Murat Tekalp,et al.  Reversible data hiding , 2002, Proceedings. International Conference on Image Processing.