Abstract

This paper suggests the Superior SOM (SSOM) based Minute Significant Watermark Generator & Detector (MSWG&D) system. RGB features of the host image are trained in different SSOM networks. Subsequent to SSOM training process, microscopic significant values are synthesized from host image and self-possessed as watermark values. Then these values are embedded into the high frequency sub band of Discrete Wavelet Transform (DWT). The Quality of invisible watermarking is proved by evaluating PSNR & Jaccard Similarity Ratio values between original and watermarked image. MSWG&D system is robust to JPEG compression and noise attacks. The experimental results prove that the strength of proposed watermarking system is ‘one more landmark’ in the watermarking techniques.

References

• Bibi Isac and V. Santhi, “A Study on Digital Image and Video Watermarking Schemes using Neural Networks”: In International Journal of Computer Applications (0975-8887). Volume 12-No.9. January 2011.
• Chenthalir N, Dr. E. Ramaraj: “Magnitude of Self Systematizing Resemblance Measures in Knowledge Mining: In Software Technology and Engineering Proceedings of the International Conference on ICSTE 2009, pp.239- 3, DOI No:10.1142/97889814289986_0044, World Scientific Publications.
• N. Chenthalir Indra and Dr. E. Ramaraj, “Similar - Dissimilar Victor Measure Analysis to Improve Image Knowledge Discovery Capacity of SOM “.: In International Conference on Information and Communication Technologies, ICT 2010 Proceedings. Volume 101, Part 2, pp.389-393, DOI: 0.1007/978-3-642-15766-0_61. published by Springer-Verlag.
• Chuan-Yu Chang, Sheng-Jyun Su, Hung-Jen Wang : “Using a Full Counterpropagation Neural Network for Image Watermarking”, International Computer Symposium, Dec. 15- 7, 2004,pp.461-466 Taipei, Taiwan.
• Chuan-Yu Chang, Sheng-Jyun Su, Hung-Jen Wang : “Copyright authentication for images with a Full Counter propagation neural network”, Expert Systems with applications 37, 2010.
• Dorina Marghescu, MikkoRajanen, Barbro Back, : “Evaluating the Quality of uses of Visual Data Mining Tools”. In: Proceedings of 11th European Conference on Information Technology Evaluation, pp.239-250, (2004).
• Fan Zhang, Hongbin Zhang, “Applications of Neural Network to Watermarking Capacity”, International Symposium on communications and Information Technologies, October 26-29, 2004.
• Jun Zhang, Nenchao Wang, Feng Xiong, “Hiding a Logo Wateramrk into the Multiwavelet Domain using Neural Networks”, In the Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence, 2002.
• Kumar, S., Raman, B., Thakur, M.: “Real Coded Genetic Algorithm based Stereo image Watermarking”. In: IJSDIA 1(1), pp.23–33 (2009).
• S.S. Sujatha and M. Mohamed Sathik “Feature Based Watermarking Algorithm by Adopting Arnold Transform ICT 2010, CCIS 101, pp. 78–82, 2010. © Springer-Verlag Berlin Heidelberg 2010.
• S.S. Sujatha. M. Mohamed Sathik, CiiT Inernational Journal of Digital Image Processing, Vol 2, No 7, pp.185-188, July 2010.