Support Vector Machine based Intelligent Watermark Decoding for Anticipated Attack
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.
[1] I.J. Cox, M.L. Miller, J.A. Bloom, Digital Watermarking and
Fundamentals, Morgan Kaufmann, San Francisco, 2002.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] S. K─▒rb─▒z, Y. Yaslan, B. G├╝nsel, Robust Audio Watermark Decoding By
Nonlinear Classification, Multimedia Signal Processing and Pattern
Recognition Lab, 2005.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] C. Staelin, "Parameter selection for support vector machines," Technical
report, HP Labs, Israel, 2002.
[15] B. Moghaddam, and M.H. Yang, "Learning Gender with support faces,"
in IEEE Transaction on Pattern Analysis and Machine Learning, vol. 24,
2002.
[16] R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern Classification," John
Wiley & Sons, Inc., New York, 2nd edition, 2001.
[17] Hsiang-Cheh Huang, Lakhmi C. Jain, Jeng-Shyang Pan, Intelligent
Watermarking Techniques, World Scientific Pub Co Inc, 2004.
[18] 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/.
[19] A. Majid, "Optimization and combination of classifiers using Genetic
Programming," PhD Thesis, Faculty of Computer Science, GIK institute,
Pakistan. Dec. 2005.
[20] MATLAB 7.0, Mathworks, http://www.mathworks.com.
[1] I.J. Cox, M.L. Miller, J.A. Bloom, Digital Watermarking and
Fundamentals, Morgan Kaufmann, San Francisco, 2002.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] S. K─▒rb─▒z, Y. Yaslan, B. G├╝nsel, Robust Audio Watermark Decoding By
Nonlinear Classification, Multimedia Signal Processing and Pattern
Recognition Lab, 2005.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] C. Staelin, "Parameter selection for support vector machines," Technical
report, HP Labs, Israel, 2002.
[15] B. Moghaddam, and M.H. Yang, "Learning Gender with support faces,"
in IEEE Transaction on Pattern Analysis and Machine Learning, vol. 24,
2002.
[16] R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern Classification," John
Wiley & Sons, Inc., New York, 2nd edition, 2001.
[17] Hsiang-Cheh Huang, Lakhmi C. Jain, Jeng-Shyang Pan, Intelligent
Watermarking Techniques, World Scientific Pub Co Inc, 2004.
[18] 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/.
[19] A. Majid, "Optimization and combination of classifiers using Genetic
Programming," PhD Thesis, Faculty of Computer Science, GIK institute,
Pakistan. Dec. 2005.
[20] MATLAB 7.0, Mathworks, http://www.mathworks.com.
@article{"International Journal of Information, Control and Computer Sciences:62297", author = "Syed Fahad Tahir and Asifullah Khan and Abdul Majid and Anwar M. Mirza", title = "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.", keywords = "Bit Correct Ratio (BCR), Grid Search, Intelligent
Decoding, Jackknife Technique, Support Vector Machine (SVM),
Watermarking.", volume = "2", number = "9", pages = "3195-6", }
