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Information Sciences
Volume 178, Issue 14, 15 July 2008, Pages 2950-2959
 
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doi:10.1016/j.ins.2008.03.011    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2008 Elsevier Inc. All rights reserved.

The upper and lower bounds of the information-hiding capacity of digital images

Fan Zhanga, Corresponding Author Contact Information, E-mail The Corresponding Author, Zhigeng Panb, Kui Caoa, Fengbin Zhenga and Fangming Wua

aCollege of Computer and Information Engineering, Henan University, Kaifeng 475001, PR China bState Key Laboratory of CAD & CG, Zhejiang University, Hangzhou 310027, PR China

Received 5 July 2007; 
revised 15 February 2008; 
accepted 22 March 2008. 
Available online 1 April 2008.

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Abstract

The information-hiding capacity of a digital image is the maximum information that can be hidden in that image, while the lower limit of information hiding is the minimum detectable information capacity. This paper proposes a new method of information-hiding capacity bounds analysis that is based on the neural network theories of attractors and attraction basins. With this method, the processes for determining upper and lower limits of information hiding, are unified within a single theoretical framework. Results of research show that the theory of attraction basins of neural networks can be used to determine the upper limit of information hiding and the theory of attractors of neural networks can be used to determine the lower limit of information hiding.

Keywords: Attraction basin; Attractors; Capacity; Information hiding; Neural network

Article Outline

1. Introduction
2. Hopfield neural network
3. An information-hiding algorithm based on neural network theory
4. Information-hiding capacity
5. The minimum detectable information capacity
6. Conclusions
Acknowledgements
Appendix A. Derivation of inequality used in Eq. (9)
References







Information Sciences
Volume 178, Issue 14, 15 July 2008, Pages 2950-2959
 
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