Digital image watermarking on triange grid of feature points
A.V. Verichev, V.A. Fedoseev

Image Processing Systems Institute, Russian Academy of Sciences,
Samara State Aerospace University

Full text of article: Russian language.

Abstract:
The paper presents a digital watermarking scheme robust to geometric distortions of an image. The proposed scheme is based on Delaunay tessellation built on a set of feature points. Various feature points extraction methods are outlined and the best one for the watermarking scheme is chosen. Embedding and extracting algorithms are presented, emphasize being on the procedures of perceptual masking of the embedded watermark according to the human visual system. Numerical experiments are performed to demonstrate robustness of the proposed watermarking scheme to a wide range of geometric distortions.

Key words:
digital watermarking, robust watermarking, geometric distortions, Delaunay triangulation, feature points, Harris detector, SIFT.

References:

  1. Barni, M. Watermarking Systems Engineering: Enabling Digital Assets Security and Other Applications / M. Barni, F. Bartolini. – Marcel Dekker, 2004.
  2. Zheng, D. RST-invariant digital image watermarking based on log-polar mapping and phase correlation / D. Zheng, J. Zhao, A.E. Saddik // Circuits and Systems for Video Technology, IEEE Transactions on. – 2003. – Vol. 13(8). – P. 753-765.
  3. Glumov, N.I. The New Blockwise Algorithm for Large-Scale Images Robust Watermarking / N.I. Glumov, V.A. Mitekin // Proceedings of ICPR-2010. – 2010. – P. 1453-1456.
  4. Glumov, N.I. The algorithm for large-scale images robust watermarking using blockwise processing / N.I. Glumov, V.A. Mitekin // Computer Optics. – 2011. – Vol. 35(3). – P. 368-372. – (In Russian).
  5. Harris, C. A combined corner and edge detector / C. Harris, M. Stephens // In Alvey Vision Conference. – 1988. – Vol. 15. – P. 50.
  6. Achard-Rouquet, C. Un détecteur de points caractéris­tiques sur des images multispectrales, extension vers un détecteur sub-pixellique / C. Achard-Rouquet, E. Bigorgne, J. Devars // GRETSI. – 1999. – P. 627-630.
  7. Lowe, D.G. Distinctive image features from scale-invariant keypoints / D.G. Lowe // International Journal of Computer Vision. – 2004. – Vol. 60(2). – P. 91-110.
  8. Matas, J. Robust wide baseline stereo from maximally stable extremal regions / J. Matas, O. Chum, M. Urba, T. Pajdla // Proceedings of British Machine Vision Conference. – 2002. – P. 384-396.
  9. Tuytelaars, T. Local invariant feature detectors: a survey / T. Tuytelaars, K. Mikolajczyk // Foundations and Trends in Computer Graphics and Vision. – 2008. – Vol. 3(3). – P. 177-280.
  10. Bas, P. Geometrically invariant watermarking using feature points / P. Bas, J-M. Chassery, B. Macq // IEEE Transactios on Image Processing. – 2002. – Vol. 11(9). – P. 1014-1028.
  11. Szeliski, R. Computer vision: algorithms and applications / R. Szeliski. – Springer, 2010.
  12. Waterloo Grey Set. – University of Waterloo Fractal coding and analysis group: Mayer Gregory Image Repository, 2009. [http://links.uwaterloo.ca/Repository.html .
  13. Skvortsov, A.V. Delaunay tessellation and its application. / A.V. Skvortsov. – Tomsk: Tomsk University Press, 2002. – 128 p. – (In Russian).
  14. Wang, Z. Image quality assessment: From error measurement to structural similarity / Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli // IEEE Transactios on Image Processing. – 2004. – Vol. 13(1).
  15. Piva, A. DCT-based watermark recovering without resorting to the uncorrupted original image / A. Piva, M. Barni, F. Bartolini, V. Cappellini // 1997 Proceedings of IEEE International Conference on Image Processing. – 1997. – Vol. 1. – P. 520-523.
  16. Voloshynovskiy, S. A stochastic approach to content adaptive digital image watermarking / S. Voloshynovskiy, A. Her­rigel, N. Baumgaertner, T. Pun // Information Hiding. – 2000. – P. 211-236.
  17. Wu, J. Pattern Masking Estimation in Image With Structural Uncertainty / J. Wu, W. Lin, G. Shi, Xiaotian Wang, and Fu Li // IEEE Transaction on Image Processing. – 2013. – Vol. 22(12). – P. 4892-4904.
    © 2009, IPSI RAS
    Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; e-mail: ko@smr.ru; Phones: +7 (846 2) 332-56-22, Fax: +7 (846 2) 332-56-20