An Artificial Intelligent Technique for Robust Digital Watermarking in Multiwavelet Domain
In this paper, an artificial intelligent technique for
robust digital image watermarking in multiwavelet domain is
proposed. The embedding technique is based on the quantization
index modulation technique and the watermark extraction process
does not require the original image. We have developed an
optimization technique using the genetic algorithms to search for
optimal quantization steps to improve the quality of watermarked
image and robustness of the watermark. In addition, we construct a
prediction model based on image moments and back propagation
neural network to correct an attacked image geometrically before the
watermark extraction process begins. The experimental results show
that the proposed watermarking algorithm yields watermarked image
with good imperceptibility and very robust watermark against various
image processing attacks.
[1] B. Chen and G. Wornell, "Quantization Index Modulation: A Class of
Provably Good Methods for Digital Watermarking and Information
Embedding," IEEE Trans. on Information Theory, vol. 47 no. 4, pp.
1423-1443, May 2001.
[2] Q. Li, C. Yuan and Y. Zhong, "Adaptive DWT-SVD Domain Image
Watermarking Using Human Visual Model," Proc. the 9th Advanced
Communication Technology, Vol. 3, pp. 1947-1951, Feb, 2007.
[3] P. Dong, J. G. Brankov, N. P. Galatsanos, Y. Yang, and F. Davoine,
"Digital Watermarking Robust to Geometric Distortions," IEEE Trans.
Image Process., vol. 14, no. 12, pp. 2140-2150, 2005.
[4] G. Gao and G. Jiang, "Grayscale Watermarking Resistant to Geometric
Attacks Based on Lifting Wavelet Transform and Neural Network,"
Proc. the 8th World Congress on Intelligent Control and Automation,
Vol. 1, pp. 1305-1310, July, 2010.
[5] X. Y. Wang, H. Y. Yang and C. Y. Cui, "An SVM-Based Robust Digital
Image Watermarking Against Desynchronization Attacks," Signal
Processing, vol. 88, No. 9, pp. 2193-2205, Sept. 2008.
[6] S. Huang, W. Zhang, W. Feng and H. Yang, "Blind Watermarking
Scheme Based on Neural Network," Proc. the 7th World Congress on
Intelligent Control and Automation, Vol. 1, pp. 5985-5989, June, 2008.
[7] L. Chen, Z. Yao, L. Chen and J. Chen, "A Novel Watermarking Detector
Based on Confidence Evaluation and Neural Network," Proc. The 1st
International Conference on Information Science and Engineering, Vol.
1, pp. 1091-1094, June, 2008.
[8] L. Ghouti, A. Bouridane, M. K. Ibrahim and S. Boussakta, "Digital
image watermarking using balanced multiwavelets," IEEE Trans. on
Signal Processing, vol. 54, pp. 1519-1536, Apr. 2006.
[9] P. Kumsawat, K. Attakitmongcol, A. Srikaew, "A Robust Image
Watermarking Scheme Using Multiwavelet Tree," Proc. World
Congress on Engineering, vol. 1, pp. 612-617, July 2007.
[10] K. Attakitmongcol, D. P. Hardin and D. M. Wilkes, "Multiwavelet
Prefilters II: Optimal Orthogonal Prefilters", IEEE Trans. on Image
Processing, vol. 10, pp. 1476-1487, Oct.2001.
[11] P. Kumsawat, K. Attakitmongcol and A. Srikaew, "A New Approach for
Optimization in Image Watermarking by Using Genetic Algorithms,"
IEEE Trans. on Signal Processing, vol.53, pp. 4707-4719, Dec. 2005.
[12] J. H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor,
MI: Univ. of Michigan Press, 1975.
[13] Z. Wang and A. C. Bovik, "A Universal Image Quality Index,"IEEE
Signal Processing Letters, vol. 9, pp. 81-84, Mar. 2002.
[14] A. T. Jones, Artificial Intelligence: A Systems Approach, Infinite
Science Press, 2008.
[15] J. Flusser, T. Suk, and B. Zitova, Moments and Moment Invariants in
Pattern Recognition, John Wiley & Sons Ltd., 2009.
[1] B. Chen and G. Wornell, "Quantization Index Modulation: A Class of
Provably Good Methods for Digital Watermarking and Information
Embedding," IEEE Trans. on Information Theory, vol. 47 no. 4, pp.
1423-1443, May 2001.
[2] Q. Li, C. Yuan and Y. Zhong, "Adaptive DWT-SVD Domain Image
Watermarking Using Human Visual Model," Proc. the 9th Advanced
Communication Technology, Vol. 3, pp. 1947-1951, Feb, 2007.
[3] P. Dong, J. G. Brankov, N. P. Galatsanos, Y. Yang, and F. Davoine,
"Digital Watermarking Robust to Geometric Distortions," IEEE Trans.
Image Process., vol. 14, no. 12, pp. 2140-2150, 2005.
[4] G. Gao and G. Jiang, "Grayscale Watermarking Resistant to Geometric
Attacks Based on Lifting Wavelet Transform and Neural Network,"
Proc. the 8th World Congress on Intelligent Control and Automation,
Vol. 1, pp. 1305-1310, July, 2010.
[5] X. Y. Wang, H. Y. Yang and C. Y. Cui, "An SVM-Based Robust Digital
Image Watermarking Against Desynchronization Attacks," Signal
Processing, vol. 88, No. 9, pp. 2193-2205, Sept. 2008.
[6] S. Huang, W. Zhang, W. Feng and H. Yang, "Blind Watermarking
Scheme Based on Neural Network," Proc. the 7th World Congress on
Intelligent Control and Automation, Vol. 1, pp. 5985-5989, June, 2008.
[7] L. Chen, Z. Yao, L. Chen and J. Chen, "A Novel Watermarking Detector
Based on Confidence Evaluation and Neural Network," Proc. The 1st
International Conference on Information Science and Engineering, Vol.
1, pp. 1091-1094, June, 2008.
[8] L. Ghouti, A. Bouridane, M. K. Ibrahim and S. Boussakta, "Digital
image watermarking using balanced multiwavelets," IEEE Trans. on
Signal Processing, vol. 54, pp. 1519-1536, Apr. 2006.
[9] P. Kumsawat, K. Attakitmongcol, A. Srikaew, "A Robust Image
Watermarking Scheme Using Multiwavelet Tree," Proc. World
Congress on Engineering, vol. 1, pp. 612-617, July 2007.
[10] K. Attakitmongcol, D. P. Hardin and D. M. Wilkes, "Multiwavelet
Prefilters II: Optimal Orthogonal Prefilters", IEEE Trans. on Image
Processing, vol. 10, pp. 1476-1487, Oct.2001.
[11] P. Kumsawat, K. Attakitmongcol and A. Srikaew, "A New Approach for
Optimization in Image Watermarking by Using Genetic Algorithms,"
IEEE Trans. on Signal Processing, vol.53, pp. 4707-4719, Dec. 2005.
[12] J. H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor,
MI: Univ. of Michigan Press, 1975.
[13] Z. Wang and A. C. Bovik, "A Universal Image Quality Index,"IEEE
Signal Processing Letters, vol. 9, pp. 81-84, Mar. 2002.
[14] A. T. Jones, Artificial Intelligence: A Systems Approach, Infinite
Science Press, 2008.
[15] J. Flusser, T. Suk, and B. Zitova, Moments and Moment Invariants in
Pattern Recognition, John Wiley & Sons Ltd., 2009.
@article{"International Journal of Information, Control and Computer Sciences:49979", author = "P. Kumsawat and K. Pasitwilitham and K. Attakitmongcol and A. Srikaew", title = "An Artificial Intelligent Technique for Robust Digital Watermarking in Multiwavelet Domain", abstract = "In this paper, an artificial intelligent technique for
robust digital image watermarking in multiwavelet domain is
proposed. The embedding technique is based on the quantization
index modulation technique and the watermark extraction process
does not require the original image. We have developed an
optimization technique using the genetic algorithms to search for
optimal quantization steps to improve the quality of watermarked
image and robustness of the watermark. In addition, we construct a
prediction model based on image moments and back propagation
neural network to correct an attacked image geometrically before the
watermark extraction process begins. The experimental results show
that the proposed watermarking algorithm yields watermarked image
with good imperceptibility and very robust watermark against various
image processing attacks.", keywords = "Watermarking, Multiwavelet, Quantization index
modulation, Genetic algorithms, Neural networks.", volume = "5", number = "12", pages = "1493-6", }