Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Geometric attack resistant image watermarking based on MSER

  • Research article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Geometric distortions are simple and effective attacks rendering many watermarking methods useless. They make detection and extraction of the embedded watermark difficult or even impossible by destroying the synchronization between the watermark reader and the embedded watermark. In this paper, we propose a blind content-based image watermarking scheme against geometric distortions. Firstly, the MSER detector is adopted to extract a set of maximally stable extremal regions which are affine covariant and robust to geometric distortions and common signal processing. Secondly, every original MSER is fitted into an elliptical region that was proved to be affine invariant. In order to achieve rotation invariance, an image normalization process is performed to transform the elliptical regions into circular ones. Finally, watermarks are repeatedly embedded into every circular disk by modifying the wavelet transform coefficients. Experimental results on standard benchmark demonstrate that the proposed scheme is robust to geometric distortions as well as common signal processing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Petitcolas F. Watermarking schemes evaluation. IEEE Signal Process, 2000, 17(5): 58–64

    Article  Google Scholar 

  2. Podilchuk C, Delp E. Digital watermarking: algorithms and applications. IEEE Signal Process, 2001, 18(4): 33–46

    Article  Google Scholar 

  3. Petitcolas F, Anderson R, Kuhn M. Attacks on copyright marking systems. Lecture Notes in Computer Science, 1998, 1525: 218–238

    Article  Google Scholar 

  4. Qi X, Qi J. A robust content-based digital image watermarking scheme. Signal Processing, 2007, 87(6): 1264–1280

    Article  MATH  Google Scholar 

  5. Gao X, Deng C, Li X, Tao D. A local tchebichef moments-based robust image watermarking. Signal Processing, 2009, 89(8): 1531–1539

    Article  MATH  Google Scholar 

  6. O’Ruanaidh J, Pun T. Rotation, scale, and translation invariant spread spectrum digital image watermarking. Signal Processing, 1998, 66(3): 303–317

    Article  MATH  Google Scholar 

  7. Song H, Yu S, Yang X, Song L, Wang C. Contourlet-based image adaptive watermarking. Signal Processing: Image Communication, 2008, 23(3): 162–178

    Article  Google Scholar 

  8. Xiao M, Wan X, Gan C, Du B. A robust dct domain watermarking algorithm based on chaos system. In: Proceedings of SPIE, 2009, 7495

    Google Scholar 

  9. Pereira S, Pun T. Robust template matching for affine resistant image watermarks. IEEE Transactions on Image Process, 2000, 9(6): 1123–1129

    Article  Google Scholar 

  10. Qi X, Qi J. Improved affine resistant watermarking by using robust templates. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. 2004, 495–408

    Google Scholar 

  11. Alghoniemy M, Tewfik A. Geometric invariance in image watermarking. IEEE Transactions on Image Process, 2004, 13(2): 145–153

    Article  Google Scholar 

  12. Dong P, Brankov J, Galatsanos N, Yang Y, Davoine F. Digital watermarking robust to geometric distortions. IEEE Transactions on Image Process, 2005, 14(12): 2140–2150

    Article  Google Scholar 

  13. Zhang H, Shu H, Coatrieux G, Zhu J, Wu Y Z J, Zhu H, Luo L. Affine legendre moment invariants for image watermarking robust to geometric distortions. IEEE Transactions on Image Process, 2011, 20(8): 2189–2199

    Article  MathSciNet  Google Scholar 

  14. Reiss T. The revised fundamental theorem of moment invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8): 830–834

    Article  Google Scholar 

  15. Flusser J, Suk T. Pattern recognition by affine moment invariants. In: IEEE International Conference on Pattern Recognition. 1993, 167–174

    Google Scholar 

  16. Bas P, Chassery J, Macq B. Geometrically invariant watermarking using feature points. IEEE Transactions on Image Process, 2002, 11(9): 1014–1028

    Article  Google Scholar 

  17. Lee H, Kim H. Robust image watermarking using local invariant features. Optical Engineering, 2006, 45(3): 1–11

    Google Scholar 

  18. Li L, Qian J, Pan J. High capacity watermark embedding based on local invariant features. In: IEEE International Conference on Multimedia and Expo. 2010, 1311–1314

    Google Scholar 

  19. Li L, Qian J, Pan J. Characteristic region based watermark embedding with rst invariance and high capacity. International Journal of Electronics and Communications, 2011, 65(5): 435–442

    Article  Google Scholar 

  20. Seo J, Yoo C. Localized image watermarking based on feature points of scale-space representation. In: IEEE International Conference Computer Vision Pattern Recognition. 2004, 1365–1375

    Google Scholar 

  21. Seo J, Yoo C. Image watermarking based on invariant regions of scalespace representation. IEEE Transactions on Signal Processing, 2006, 54(4): 1537–1549

    Article  Google Scholar 

  22. Terzija N, Geisselhardt W. A novel synchronisation approach for digital image watermarking based on scale invariant feature point detector. In: IEEE International Conference on Image Processing. 2006, 2585–2588

    Google Scholar 

  23. Gao X, Deng C, Li X, Tao D. Geometric distortion insensitive image watermarking in affine covariant regions. IEEE Transactions on Systems. Man. and Cybernetics Society, 2010, 40(3): 278–286

    Article  Google Scholar 

  24. Matas J, Chum O, Urban M, Pajdla T. Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference. 2004, 36–43

    Google Scholar 

  25. Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Gool L V. A comparison of affine region detectors. Internation Journal Of Computer Vision, 2005, 65(1-2): 43–72

    Article  Google Scholar 

  26. Matas J, Petr B, Chum O. Rotational invariants for wide-baseline stereo. Research Report of CMP, 2003

    Google Scholar 

  27. Lu S, Sun W, Hsu Y, Chang C. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Multimedia, 2006, 8(4): 668–685

    Article  Google Scholar 

  28. Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Process, 2004, 13(4): 600–612

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Xuejuan Zhang & Jingjie Li

  2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Xiaochun Cao

Authors
  1. Xuejuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaochun Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingjie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaochun Cao.

Additional information

Xuejuan Zhang received the BE degree in computer science and technology from Dalian Nationalities Uninversity, Dalian, Liaoning, China. She is currently pursuing the ME degree with School of Computer Science and Technology, Tianjin University. She has been working as a research assistant in the Computer Vision Lab since September 2010. Her research interests lie in the computer vision field covering mainly image processing, multimedia forensic, and pattern recognition.

Xiaochun Cao received the BE and ME degrees, both in computer science, from Beihang University, Beijing, China. He received the PhD degree in computer science from the University of Central Florida, Orlando. After graduation, he spent about three years at ObjectVideo Inc. as a research scientist. Then he joined the School of Computer Science and Technology, Tianjin University, China, where he was a professor (2008–2012). He was elected into the 100 Talents Program of Chinese Academy of Sciences (CAS) and joined the Institute of Information Engineering, CAS in October, 2012. His research interests are computer vision, image processing, and information forensic and security. He has authored and coauthored over 50 peer-reviewed journal and conference papers. In 2004 and 2010, Dr. Cao was the recipient of the Piero Zamperoni best student paper award at the International Conference on Pattern Recognition.

Jingjie Li received the BS degree in faculty of science from China University of Mining and Technology, Xuzhou, Jiangsu, China. He is currently pursuing the ME degree with School of Computer Science and Technology, Tianjin University. He has been working as a research assistant in the Computer Vision Lab since July 2010. His research interests include computer vision, computer graphics, image retrieval, and multimedia forensics.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, X., Cao, X. & Li, J. Geometric attack resistant image watermarking based on MSER. Front. Comput. Sci. 7, 145–156 (2013). https://doi.org/10.1007/s11704-013-2174-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-013-2174-7

Keywords

Search

Navigation