Commenced in January 2007
Paper Count: 30184
Support Vector Machine based Intelligent Watermark Decoding for Anticipated Attack
Abstract:In this paper, we present an innovative scheme of blindly extracting message bits from an image distorted by an attack. Support Vector Machine (SVM) is used to nonlinearly classify the bits of the embedded message. Traditionally, a hard decoder is used with the assumption that the underlying modeling of the Discrete Cosine Transform (DCT) coefficients does not appreciably change. In case of an attack, the distribution of the image coefficients is heavily altered. The distribution of the sufficient statistics at the receiving end corresponding to the antipodal signals overlap and a simple hard decoder fails to classify them properly. We are considering message retrieval of antipodal signal as a binary classification problem. Machine learning techniques like SVM is used to retrieve the message, when certain specific class of attacks is most probable. In order to validate SVM based decoding scheme, we have taken Gaussian noise as a test case. We generate a data set using 125 images and 25 different keys. Polynomial kernel of SVM has achieved 100 percent accuracy on test data.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080808Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1338
 I.J. Cox, M.L. Miller, J.A. Bloom, Digital Watermarking and Fundamentals, Morgan Kaufmann, San Francisco, 2002.
 Piva, M. Barni, F. Bartolini, V. Cappellini, "DCT-based watermark recovering without resorting to the uncorrupted original image," Proc Int. Conf. Image Processing, Oct. 1997, vol. 1 pp. 520-523.
 S. Lyu and H. Farid, Detecting hidden messages using high-order statistics and support vector machines, 5th international workshop on Information Hiding, Noordwijkerhout, The Netherlands, 2002.
 Y. Fu, R. Shen and H. Lu, Optimal watermark detection based on support vector machines, Proc. of International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, pp.552-557.
 P.T. Yu, H.H. Tsai, J.S. Lin, Digital watermarking based on neural networks for color images, Signal Processing, Elsevier Science, 81, 663- 671, 2001.
 Asifullah Khan "A Novel Approach to decoding: Exploiting anticipated attack information using Genetic programming." International Journal of knowledge based and Intelligent engineering Systems 9(2006) pp. 1-10.
 J.R. Hernandez, M. Amado, F. Perez-Gonzalez, DCT-Domain watermarking techniques for still images: Detector performance analysis and a new structure, IEEE Trans. Image Process. 9 (1) (2000) 55-68.
 Zhang Li1, SamKwong2, Marian Choy2, Wei-wei Xiao 1, Ji Zhen1, and Ji-hong Zhang1, An Intelligent Watermark Detection Decoder Based on Independent Component Analysis, DOCIS Documents in Computing and Information Science 2003.
 S. K─▒rb─▒z, Y. Yaslan, B. G├╝nsel, Robust Audio Watermark Decoding By Nonlinear Classification, Multimedia Signal Processing and Pattern Recognition Lab, 2005.
 Veera Venkataramani, Shantanu Chakrabartty, and William Byrne. Support Vector Machines For Segmental Minimum Bayes Risk Decoding Of Continuous Speech, 2003 IEEE Automatic Speech Recognition and Understanding Workshop.
 Asifullah Khan, Anwar M. Mirza, Genetic perceptual shaping: Utilizing cover image and conceivable attack information during watermark embedding, Sep.2005. Elsevier. Journal of Information Fusion.
 O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, "Choosing Multiple Parameters for Support Vector Machines, Machine Learning," vol. 46, No. 1-3, pp. 131-159, 2002.
 C.W. Hsu, C.C. Chang, and C.J. Lin, "A practical guide to support vector machines," Technical report, Department of Computer Science & Information Engineering, National Taiwan University, 2003.
 C. Staelin, "Parameter selection for support vector machines," Technical report, HP Labs, Israel, 2002.
 B. Moghaddam, and M.H. Yang, "Learning Gender with support faces," in IEEE Transaction on Pattern Analysis and Machine Learning, vol. 24, 2002.
 R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern Classification," John Wiley & Sons, Inc., New York, 2nd edition, 2001.
 Hsiang-Cheh Huang, Lakhmi C. Jain, Jeng-Shyang Pan, Intelligent Watermarking Techniques, World Scientific Pub Co Inc, 2004.
 C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
 A. Majid, "Optimization and combination of classifiers using Genetic Programming," PhD Thesis, Faculty of Computer Science, GIK institute, Pakistan. Dec. 2005.
 MATLAB 7.0, Mathworks, http://www.mathworks.com.