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Reseach Article

An Approach of an Image Watermarking Scheme using Neural Network

by Asmaa Qasim Shareef, Roaa Essam Fadel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 1
Year of Publication: 2014
Authors: Asmaa Qasim Shareef, Roaa Essam Fadel
10.5120/15977-4872

Asmaa Qasim Shareef, Roaa Essam Fadel . An Approach of an Image Watermarking Scheme using Neural Network. International Journal of Computer Applications. 92, 1 ( April 2014), 44-48. DOI=10.5120/15977-4872

@article{ 10.5120/15977-4872,
author = { Asmaa Qasim Shareef, Roaa Essam Fadel },
title = { An Approach of an Image Watermarking Scheme using Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 1 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 44-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number1/15977-4872/ },
doi = { 10.5120/15977-4872 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:13:11.773216+05:30
%A Asmaa Qasim Shareef
%A Roaa Essam Fadel
%T An Approach of an Image Watermarking Scheme using Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 1
%P 44-48
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An approach of an image watermarking scheme using neural network is presented in this work. In the proposed approach, watermark comes from the weights of an identify image that are loaded from a learned feed-forward neural network, the neural network is learned by using the back-propagation learning algorithm. To improve the robustness of watermarked image; the procedure of watermark embedding is embedded into host image through selecting and modifying the Gaussian coefficients comes from a noisy image. The noised image is damaged by the salt and pepper noise. In order to identify the cover of extracted watermark, feed-forward neural network is used in the watermarking identification to overcome the limitation of unknown data comes randomly. The results of the scheme realization show the robustness of proposed scheme that has preferable performance for both identification and watermarking of a noised image

References
  1. Fan Z. , Hongbin Z. . 2004. CAPACTY AND RELIABILITY OF DIGITAL WATERMARKINGIEEE International Conference on the Business of Electronic Product Reliability and Liability, pp. 162- 165,
  2. Cummins J. , Diskin P. , Lau S. and ParletR. t. 2004. STEGANOGRAPHY AND DIGITAL WATERMARKING http://www. gnu. org/copyleft/fdl. html
  3. Shareef A. Q. . 1995. NEURAL NETWORKS FOR SYSTEM IDENTIFICATION. Ms. c. thesis,University of Technology, Control and System Dep. .
  4. Hoffmann G. . 2002. GAUSSIAN FILTER http://www. fho-emden. de/~hoffmann/
  5. Steinebach M. , Hauer E. , Wolf P. . 2007. EFFICIENT WATERMARKING STRATIGES. Third International Conference on Automated Production of Cross Media Content for Multi-channel Distribution, IEEE: 0-7695-3030-3/07, pp. 65-71.
  6. Schyndel R. G. van, lTrkel A. Z. l, Osbome C. F1994. A DIGITAL WATERMARK. IEEE International Conference Image Processing Proceedings ICIP-94, Vol. 2, pp. 86-90.
  7. Wong K. K. , Tse C. H. , K. S. Ng, Lee T. H. and Cheng L. M. 1997. ADAPTIVE WATER MARKING. IEEE Transactions on Consumer Electronics, Vol. 43, No. 4, pp. 1003-1009.
  8. Joshi A. M. , . Darji A, . Mishr V. 2011. DESIGN AND IMPLEMENTATION OF REAL-TIME IMAGEWATERMARKING. IEEE International Conference pp. 1-5, 14-16.
  9. Chinnasarn K. , Rangsanseri Y. , Thitimaishima P. 1998. REMOVING SALT-AND-PEPPER NOISE INTEXT/GRAPHICS IMAGES. IEEE Asia-Pacific Conference on APCCAS, Circuits and Systems Journal. pp. 459-462.
  10. Krishn S. G. , Reddy T. S. , Rajini G. K. . 2012. REMOVAL OF HIGH DENSITY SALT AND PEPPER NOISE THROUGH MODIFIED DECISION BASED UNSYMMETRIC TRIMMED MEDIANFILTER. International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 1, pp. 090-094. www. ijera. com.
  11. Chan R. H. , Chung-Wa Ho, and Nikolova M. . 2004. SALT-AND-PEPPER NOISE REMOVEL BY MEDIAN-TYPE NOISE DETECTORS AND DETAIL-PRESERVING REGULARIZATION http://www. math. cuhk. edu. hk/~rchan/paper/impulse/impulse. pdf.
  12. Yongqiang C. , Yanqing Z. , and Lihua P. . 2009. A DWT DOMAIN IMAGE WATERMARKING SCHEME USING GENETIC ALGORITHM AND SYNERGETIC NEURAL NETWORK. Proceedings of the International Symposium on Information Processing (ISIP'09) Huangshan, China, pp. 298-301.
  13. Bansal A. , Bhadauria S. S. . 2005 – 2008. WATERMARKING USING NEURAL NETWORK AND HIDING THE TRAINED NETWORK WITHIN THE COVER IMAGE. Journal of Theoretical and Applied Information Technology. www. jatit. org
  14. Lai F. H. L, Yong C. Z. , Long T. . 2005. NOVEL PERCEPUAL MODELING WATERMARKING WITH MLF NEURAL NETWORKS. International Journal of Information and Communication Engineering, Vol. 1, No. 5, pp. 266-269.
  15. Oueslati S. , Cherif A. , Solaimane B. . 2011 ADAPTIVE IMAGE WATERMARKING SCHEME BASED ON NEURAL NETWORK. International Journal of Engineering Science and Technology (IJEST), Vol. 3, No. 1, pp. 748-765.
Index Terms

Computer Science
Information Sciences

Keywords

feed-forward neural network salt and pepper noise Gaussian filter coefficients.