Distortion Estimation in Digital Image Watermarking using Genetic Programming
This paper introduces a technique of distortion
estimation in image watermarking using Genetic Programming (GP).
The distortion is estimated by considering the problem of obtaining a
distorted watermarked signal from the original watermarked signal as
a function regression problem. This function regression problem is
solved using GP, where the original watermarked signal is
considered as an independent variable. GP-based distortion
estimation scheme is checked for Gaussian attack and Jpeg
compression attack. We have used Gaussian attacks of different
strengths by changing the standard deviation. JPEG compression
attack is also varied by adding various distortions. Experimental
results demonstrate that the proposed technique is able to detect the
watermark even in the case of strong distortions and is more robust
against attacks.
[1] I. J. Cox, M. L. Miller, and J. A. Bloom, Digital Watermarking and
fundamentals, Morgan Kaufmann, San Francisco, 2002.
[2] M. Barni, and F. Bartolini, Watermarking systems engineering: Enabling
digital assets security and other application, Marcel Dekker, Inc. New
York, 2004.
[3] Kiryung Lee, Dong Sik Kim, Taejeong Kim, and Kyung Ae Moon, "Em
estimation of scale factor for quantization-based audio watermarking,"
in Digital Watermarking,Second International Workshop,IWDW 2003,
Seoul, Korea, Oct. 2003.
[4] A. Piva, M. Barni, F. Bartolini, V. Cappellini, DCT-based Watermark
Recovering without Resorting to the Uncorrupted Original Image, Proc
Int. Conf. Image Processing, vol. 1, Oct. 1997, pp. 520-523.
[5] J.C. Oostveen, A.A.C. Kalker, and M. Staring, "Adaptive quantize
watermarking," in Proc. of SPIE: Security, Steganography, and
Watermarking of Multimedia Contents VI, San Jose, California, USA,
2004, vol. 5306, pp. 37-39.
[6] Qiao Li, Ingemar J. Cox, Using perceptual models to improve fidelity
and provide invariance to volumetric scaling for quantization index
modulation watermarking, Campus Seminar Series, Departments of
Computer Science and Electronic and Electrical Engineering, University
College London Torrington Place, London, WC1E 7JE, England, Feb.
2005.
[7] Sviatoslav Voloshynovskiy, Frederic Deguillaume, Shelby Pereira and
Thierry Pun, Optimal adaptive diversity watermarking with channel state
estimation University of Geneva - CUI, 24 rue duGeneral Dufour, CH
1211, Geneva 4, Switzerland, 2002.
[8] A. Khan and Anwar M. Mirza, Genetic Perceptual Shaping: Utilizing
Cover Image and Conceivable Attack Information Using Genetic
Programming, accepted in International Journal of Information Fusion,
Elsevier Science, 2005.
[9] Asifullah Khan, A Novel approach to decoding: Exploiting Anticipated
Attack Information using Genetic Programming, International Journal of
Knowledge-Based Intelligent Engineering Systems, 2006, (in press).
[10] A. Khan, Anwar M. Mirza and A. Majid, Intelligent Perceptual Shaping
of a Digital Watermark: Exploiting Characteristics of Human Visual
System, accepted in the International Journal of Knowledge-Based
Intelligent Engineering Systems, 2005.
[11] W. Banzhaf, P. Nordin, R.E. Keller, and F.D. Francone, "Genetic
Programming: An Introduction," Morgan Kaufmann Publishers, CA,
1998.
[12] S. Gustafon, "An Analysis of Diversity in Genetic Programming", PhD
Thesis, University of Nottingham, UK, 2004.
[13] http://www.geneticprogramming.com.
[1] I. J. Cox, M. L. Miller, and J. A. Bloom, Digital Watermarking and
fundamentals, Morgan Kaufmann, San Francisco, 2002.
[2] M. Barni, and F. Bartolini, Watermarking systems engineering: Enabling
digital assets security and other application, Marcel Dekker, Inc. New
York, 2004.
[3] Kiryung Lee, Dong Sik Kim, Taejeong Kim, and Kyung Ae Moon, "Em
estimation of scale factor for quantization-based audio watermarking,"
in Digital Watermarking,Second International Workshop,IWDW 2003,
Seoul, Korea, Oct. 2003.
[4] A. Piva, M. Barni, F. Bartolini, V. Cappellini, DCT-based Watermark
Recovering without Resorting to the Uncorrupted Original Image, Proc
Int. Conf. Image Processing, vol. 1, Oct. 1997, pp. 520-523.
[5] J.C. Oostveen, A.A.C. Kalker, and M. Staring, "Adaptive quantize
watermarking," in Proc. of SPIE: Security, Steganography, and
Watermarking of Multimedia Contents VI, San Jose, California, USA,
2004, vol. 5306, pp. 37-39.
[6] Qiao Li, Ingemar J. Cox, Using perceptual models to improve fidelity
and provide invariance to volumetric scaling for quantization index
modulation watermarking, Campus Seminar Series, Departments of
Computer Science and Electronic and Electrical Engineering, University
College London Torrington Place, London, WC1E 7JE, England, Feb.
2005.
[7] Sviatoslav Voloshynovskiy, Frederic Deguillaume, Shelby Pereira and
Thierry Pun, Optimal adaptive diversity watermarking with channel state
estimation University of Geneva - CUI, 24 rue duGeneral Dufour, CH
1211, Geneva 4, Switzerland, 2002.
[8] A. Khan and Anwar M. Mirza, Genetic Perceptual Shaping: Utilizing
Cover Image and Conceivable Attack Information Using Genetic
Programming, accepted in International Journal of Information Fusion,
Elsevier Science, 2005.
[9] Asifullah Khan, A Novel approach to decoding: Exploiting Anticipated
Attack Information using Genetic Programming, International Journal of
Knowledge-Based Intelligent Engineering Systems, 2006, (in press).
[10] A. Khan, Anwar M. Mirza and A. Majid, Intelligent Perceptual Shaping
of a Digital Watermark: Exploiting Characteristics of Human Visual
System, accepted in the International Journal of Knowledge-Based
Intelligent Engineering Systems, 2005.
[11] W. Banzhaf, P. Nordin, R.E. Keller, and F.D. Francone, "Genetic
Programming: An Introduction," Morgan Kaufmann Publishers, CA,
1998.
[12] S. Gustafon, "An Analysis of Diversity in Genetic Programming", PhD
Thesis, University of Nottingham, UK, 2004.
[13] http://www.geneticprogramming.com.
@article{"International Journal of Information, Control and Computer Sciences:51498", author = "Labiba Gilani and Asifullah Khan and Anwar M. Mirza", title = "Distortion Estimation in Digital Image Watermarking using Genetic Programming", abstract = "This paper introduces a technique of distortion
estimation in image watermarking using Genetic Programming (GP).
The distortion is estimated by considering the problem of obtaining a
distorted watermarked signal from the original watermarked signal as
a function regression problem. This function regression problem is
solved using GP, where the original watermarked signal is
considered as an independent variable. GP-based distortion
estimation scheme is checked for Gaussian attack and Jpeg
compression attack. We have used Gaussian attacks of different
strengths by changing the standard deviation. JPEG compression
attack is also varied by adding various distortions. Experimental
results demonstrate that the proposed technique is able to detect the
watermark even in the case of strong distortions and is more robust
against attacks.", keywords = "Blind Watermarking, Genetic Programming (GP),
Fitness Function, Discrete Cosine Transform (DCT).", volume = "2", number = "3", pages = "685-6", }