A neural network approach to digital data hiding based on the perceptual masking model of the human vision system
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 24 August 2010
Abstract
Purpose
The purpose of this paper is to present a novel approach for digital watermarking and steganography technique that uses neural networks. The performance of the proposed solution in terms of its capacity, transparency, and robustness is investigated.
Design/methodology/approach
The proposed technique trains a neural network to learn the perceptual masking model of the human vision system. Once trained, the neural network identifies pixels whose most significant alteration will be least perceptible to the human eye. The image is then altered based on the network recommendation to include the watermark or the covert data.
Findings
Experimental results demonstrate that the proposed technique offers excellent transparency and good capacity. In addition, since neural networks store their learned knowledge in a distributed fashion, steganalysis of the image without access to the network is very difficult, if not impossible. Results demonstrate good performance of the proposed solution in terms of its capacity, transparency, and robustness.
Originality/value
Use of neural networks in extracting and representing perceptual masking model of human vision system is interesting. Value added by the proposed approach is in its use of artificial neural networks to model the perceptual masking model of human vision system for injecting imperceptible data into most perceptually significant pits of an image. The proposed approach may be used in combination with most current and popular methods with little impact on perceptual quality of the resulting image.
Keywords
Citation
Najafi, H.L. (2010), "A neural network approach to digital data hiding based on the perceptual masking model of the human vision system", International Journal of Intelligent Computing and Cybernetics, Vol. 3 No. 3, pp. 391-409. https://doi.org/10.1108/17563781011066693
Publisher
:Emerald Group Publishing Limited
Copyright © 2010, Emerald Group Publishing Limited