Abstract
This paper suggests a new block based watermarking technique utilizing preprocessing and support vector machine (PPSVMW) to protect color image’s intellectual property rights. Binary test set is employed here to train support vector machine (SVM). Before adding binary data into the original image, blocks have been separated into two parts to train SVM for better accuracy. Watermark’s 1 valued bits were randomly added into the first block part and 0 into the second block part. Watermark is embedded by modifying the blue channel pixel value in the middle of each block so that watermarked image could be composed. SVM was trained with set-bits and three other features which are averages of the differences of pixels in three distinct shapes extracted from each block, and hence without the need of original image, it could be extracted. The results of PPSVMW technique proposed in this study were compared with those of the Tsai’s technique. Our technique was proved to be more efficient.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pan, J.S., Huang, H.C., Jain, L.C.: Intelligent Watermarking Techniques. Series on innovative Intelligence, vol. 7 (2004)
Pan, J.S., Huang, H.C., Wang, F.H.: Genetic Watermarking Techniques. In: Proceed. Fifth Int. conf. inform. Engineer. Syst. Allied technol., pp. 1032–1036 (2001)
Podilchuk, C.I., Delp, E.J.: Digital Watermarking: Algorithms and Applications. IEEE signal processing magazine, 33–46 (2001)
Tsai, H.H., Sun, D.W.: Color Image Watermark Extraction Based on Support Vector Machines. Information Sciences 177, 550–569 (2007)
Li, C.H., Lu, Z.D.: Application Research on Support Vector Machine in Image Watermarking. Neural networks and Brain, 1129–1134 (2005)
Shieh, C.S., Huang, H.C., Wang, F.H.: Genetic Watermarking Based on Transform-domain Techniques. Pattern Recognition, 555–565 (2003)
Yu, P.T., Tsai, H.H., Lin, J.S.: Digital Watermarking Based on Neural Networks for Color Images. Signal Processing 81, 663–671 (2001)
Khan, A., Tahir, S.F., Majid, A., Choi, T.S.: Machine Learning Based Adaptive Watermark Decoding in View of Anticipated Attack. Pattern Recognition, 2594–2610 (2008)
Li, L.D., Guo, L.: Localized Image Watermarking in Spatial Domain Resistant to Geometric Attacks. International Journal of Electronics and Communications (Article in press)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Kulkarni, A., Jayaraman, V.K., Kulkarni, B.D.: Support Vector Classification with Parameter Tuning Assisted by Agent-based Technique. Computers and Chemical Engineering 28, 311–318 (2004)
Takeuchi, K., Collier, N.: Bio-medical Entity Extraction Using Support Vector Machines. Artificial Intelligence in Medicine 33(2), 125–137 (2003)
Chen, K.Y., Wang, C.H.: A Hybrid SARIMA and Support Vector Machines in Forecasting the Production Values of the Machinery Industry in Taiwan. Expert Systems with Applications 32(1), 254–264 (2007)
Çomak, E., Arslan, A., Türkoğlu, İ.: A Decision Support System Based on Support Vector Machines for Diagnosis of the Heart Valve Diseases. Computers in Biology and Medicine 37(1), 21–27 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fındık, O., Bayrak, M., Babaoğlu, İ., Çomak, E. (2008). Color Image Watermarking Scheme Based on Efficient Preprocessing and Support Vector Machines. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2008. Communications in Computer and Information Science, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85930-7_51
Download citation
DOI: https://doi.org/10.1007/978-3-540-85930-7_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85929-1
Online ISBN: 978-3-540-85930-7
eBook Packages: Computer ScienceComputer Science (R0)