CFP last date
20 January 2025
Reseach Article

Perceptual Image Watermarking based on a Mixed-scale Wavelet Representation

by Meina Amar, Rachid Harba, Hassan Douzi, Frderic Ros, Mohamed El Hajji, Rabia Riad, Khadija Gourrame
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Number 8
Year of Publication: 2017
Authors: Meina Amar, Rachid Harba, Hassan Douzi, Frderic Ros, Mohamed El Hajji, Rabia Riad, Khadija Gourrame
10.5120/ijca2017915196

Meina Amar, Rachid Harba, Hassan Douzi, Frderic Ros, Mohamed El Hajji, Rabia Riad, Khadija Gourrame . Perceptual Image Watermarking based on a Mixed-scale Wavelet Representation. International Journal of Computer Applications. 172, 8 ( Aug 2017), 1-9. DOI=10.5120/ijca2017915196

@article{ 10.5120/ijca2017915196,
author = { Meina Amar, Rachid Harba, Hassan Douzi, Frderic Ros, Mohamed El Hajji, Rabia Riad, Khadija Gourrame },
title = { Perceptual Image Watermarking based on a Mixed-scale Wavelet Representation },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 172 },
number = { 8 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume172/number8/28268-2017915196/ },
doi = { 10.5120/ijca2017915196 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:19:46.396631+05:30
%A Meina Amar
%A Rachid Harba
%A Hassan Douzi
%A Frderic Ros
%A Mohamed El Hajji
%A Rabia Riad
%A Khadija Gourrame
%T Perceptual Image Watermarking based on a Mixed-scale Wavelet Representation
%J International Journal of Computer Applications
%@ 0975-8887
%V 172
%N 8
%P 1-9
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Perceptual image watermarking consists in using knowledge of the Human Visual System (HVS) to choose the strength of the watermark according to image properties. This paper proposes a new perceptual image watermarking method that combines the advantages of both the wavelet domain and the spatial domain since a mixed-scale wavelet representation is applied. By considering the density of the dominant wavelet coefficients, our scheme is able to differentiate uniform, edge and texture areas. This allowed us to apply adapted luminance, edge or texture masking more efficiently. We selected effective Just Noticeable Difference models from the literature, i.e. luminance and edge masking developed by Chou and Li, and texture masking developed by Qi et al. We also took into account the HVS sensitivity, which varies with the orientation of the image activity. The method was tested on a large database of 50 color images, and compared with four other watermarking methods from the literature. Visual quality tests were conducted and the robustness to attacks was tested compared with four other watermarking methods from the literature. Results show that the proposed method yields a high visual quality and our method is very robust against attacks. This last point is of great importance for real applications. The proposed method is the best trade-off between visual quality and resistance to attacks among the tested methods.

References
  1. A. Beghdadi, M.C. Larabi, A. Bouzerdoum, and K.M. Iftekharuddin. 2013. A survey of perceptual image processing methods, Sig. Proc. Image Commun. 28(8), 811-831.
  2. R.B. Wolfgang, C.I. Podilchuk, and E.J. Delp. 1999. Perceptual watermarks for digital images and video, In: Proceedings of the IEEE, Special Issue on Identification and Protection of Multimedia Information, vol. 87, pp. 1108-1126
  3. M. Barni, and F. Bartolini. Boca Raton 2004. Watermarking Systems Engineering Enabling Digital Assets Security and Other Applications, CRC Press, ISBN: 0-8247-4806-9.
  4. I. J. Cox, M. Miller, J. Bloom, J. Fridrich, and T. Kalker. 2007. Digital watermarking and steganography. Morgan Kaufmann.
  5. H. H. Tsai, H. C. Tseng, and Y. S. Lai. 2010. Robust lossless image watermarking based on trimmed mean algorithm and support vector machine, J. Syst. Softw. 83(6), 1015-1028.
  6. L. Li, X. Yuan, Z. Lu, and J. S. Pan. 2010. Rotation invariant watermark embedding based on scale adapted characteristic regions, Inf. Sci. 180(15), 2875-2888.
  7. H. Qi, D. Zheng, and J. Zhao. 2008. Human visual system based adaptive digital image watermarking, Sig. Process. 88(1), 174-188.
  8. P. Bas, J.M. Chassery, and B. Macq. 2002. Mthode de tatouage fond sur le contenu, Traitement du Signal. 19(1), 11?18.
  9. Y. Niu, M. Kyan, L. Ma, A. Beghdadi, and S. Krishnan. 2013. Visual saliency’s modulatory effect on just noticeable distortion profile and its application in image watermarking, Sig. Process. Image Commun. 28(8), 917-928
  10. C. H. Chou, and Y. C. Li. 1995. A perceptually tuned subband image coder based on the measure of Just noticeable-distortion profile, IEEE Trans. Circ. Syst. Video Technol. 5(6), 467-476.
  11. X. Yang, W. Lin, Z. Lu, E. Ong, and S. Yao. 2005. Motioncompensated residue preprocessing in video coding based on just-noticeable-distortion profile, IEEE Trans. Circuits Syst. Video Technol. 15(6), 742-752.
  12. P. B. Nguyen, A. Beghdadi, and M. Luong,. 2013. Perceptual watermarking using a new Just-NoticeableDifference model, Sig. Proc. Image Commun. 28(10), 1506-1525.
  13. A. B. Watson. February 1993. DCT quantization matrices visually optimized for individual images, In: Proceedings of the SPIE Conference on Human Vision, Visual Processing and Digital Display IV, 1913, pp. 202-216.
  14. F. Bartolini, M. Barni, V. Cappellini, and A. Piva. October 1998. Mask building for perceptually hiding frequency embedded watermarks, in: Proceedings of the IEEE ICIP98 1 450- 454.
  15. M. Amar, R. Harba, H. Douzi, F. Ros, M. El Hajji, R. Riad and K. Gourrame. 2016. A JND Model Using a Texture-Edge Selector Based on Faber-Schauder Wavelet Lifting Scheme, In: Image and Signal Processing. Springer International Publishing, pp. 328-336.
  16. M. Barni, and F. Bartolini, Franco, and P. Alessandro. Improved wavelet-based watermarking through pixelwise masking. Image Processing, IEEE Transactions on, vol. 10, no 5, p. 783-791, 2001.
  17. J. Canny. 1986. A computational approach to edge detection. IEEE Trans.Patt. Anal.and Mach. Intell., 36:961-1005.
  18. H.Douzi, D. Mammass, and F. Nouboud. 2001. Faber- Schauder wavelet transform, application to edge detection and image characterization, J. Math. Imag. Vis. 14(2), 91-101.
  19. M. El Hajji, H. Douzi, R. Harba, D. Mammass, and F. Ros. 2012. New image watermarking algorithm based on mixed scales wavelets, J. Electron. Imaging 21(1), 1-7.
  20. H. Douzi. 2001 Base d’ondelettes de Faber-Schauder et applications au traitement d’images, PhD thesis, University of Paris 6.
  21. F. Hartung, and B. Girod. October 1997 Fast Public-Key Watermarking of Compressed Video, Proc. of IEEE Int.Conf. Image Processing, vol. 1, pp-528-531.
  22. S. Voloshynovskiy, A. Herrigel, N. Baumgaertner, and T. Pun. January 2000. A stochastic approach to content adaptive digital image watermarking, In Information Hiding, Springer Berlin Heidelberg, pp. 211-236.
  23. A. Cheddad, J. Condell, K. Curran, and M. Kevitt. 2010. Digital image steganography suvey and analysis of current methods, Signal Process, Vol 90, pp. 727-752.
  24. Z. Wang, A. C. Bovik and E. P. Simoncelli. 2005. Structural approaches to image quality assessment, Handbook of image and video processing, 2nd Edition, Al Bovik, ed, Academic Press.
  25. J.L. Blin. 2003. SAMVIQ-Subjective assessment methodology for video quality, rapport technique BPN 056, EBU Project Group B/VIM Video Multimedia.
Index Terms

Computer Science
Information Sciences

Keywords

Perceptual models Human Visual System digital watermarking wavelet lifting scheme