Advertisement

Analysis of Comparators for Binary Watermarks

  • Himanshu AgarwalEmail author
  • Balasubramanian Raman
  • Pradeep K. Atrey
  • Mohan Kankanhalli
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)

Abstract

Comparator is one of key components of watermarking system that determines its performance. However, analysis and development of comparator is an undermined objective in the field of watermarking. In this paper, the core contribution is that five comparators for binary watermarks are analysed by theory and experiments. In the analysis, it is explored that negative pair of binary watermarks provide same information. Receiver operating characteristic curve is used for experimental analysis. It is observed that comparators based on similarity measure functions of symmetric normalized Hamming similarity (SNHS) and absolute mean subtracted normalized correlation coefficient (AMSNCC) have outstanding performance. Further, a range of threshold of SNHS based comparator that maximizes decision accuracy of a watermarking system is found by theoretical analysis. This range is verified by experiments.

Keywords

Comparator Threshold Watermarking Binary watermarks Receiver operating characteristic curve 

Notes

Acknowledgements

The author, Himanshu Agarwal, acknowledges the grants of the University Grant Commission (UGC) of New Delhi, India under the JRF scheme and Canadian Bureau for International Education under the Canadian Commonwealth Scholarship Program. He also acknowledges research support of the Maharaja Agrasen Technical Education Society of India and Jaypee Institute of Information Technology of India.

References

  1. 1.
    Agarwal, H., Atrey, P. K. and Raman, B. Image watermarking in real oriented wavelet transform domain. Multimedia Tools and Applications, 74(23):10883–10921, 2015.CrossRefGoogle Scholar
  2. 2.
    Agarwal, H., Raman, B. and Venkat, I. Blind reliable invisible watermarking method in wavelet domain for face image watermark. Multimedia Tools and Applications, 74(17):6897–6935, 2015.CrossRefGoogle Scholar
  3. 3.
    Bender, W., Butera, W., Gruhl, D., et al. Applications for data hiding. IBM Systems Journal, 39(3.4):547–568, 2000.Google Scholar
  4. 4.
    Bhatnagar, G. and Raman, B. A new robust reference watermarking scheme based on DWT-SVD. Computer Standards & Interfaces, 31(5):1002–1013, 2009.Google Scholar
  5. 5.
    Cox, I. J., Kilian, J., Leighton, F. T. and Shamoon, T. Secure spread spectrum watermarking for multimedia. IEEE Transactions on Image Processing, 6(12):1673–1687, 1997.CrossRefGoogle Scholar
  6. 6.
    Kundur, D. and Hatzinakos, D. Digital watermarking for telltale tamper proofing and authentication. Proceedings of the IEEE, 87(7):1167–1180, 1999.CrossRefGoogle Scholar
  7. 7.
    Linnartz, J. P., Kalker, T. and Depovere, G. Modelling the false alarm and missed detection rate for electronic watermarks. In Information Hiding, pages 329–343, 1998.Google Scholar
  8. 8.
    Memon, N. and Wong, P. W. Protecting digital media content. Communications of the ACM, 41(7):35–43, 1998.CrossRefGoogle Scholar
  9. 9.
    Miller, M. L. and Bloom, J. A. Computing the probability of false watermark detection. In Information Hiding, pages 146–158, 2000.Google Scholar
  10. 10.
    Pandey, P., Kumar, S. and Singh, S. K. Rightful ownership through image adaptive DWT-SVD watermarking algorithm and perceptual tweaking. Multimedia Tools and Applications, 72(1):723–748, 2014.CrossRefGoogle Scholar
  11. 11.
    Rani, A., Raman, B., Kumar, S. A robust watermarking scheme exploiting balanced neural tree for rightful ownership protection. Multimedia Tools and Applications, 72(3):2225–2248, 2014.CrossRefGoogle Scholar
  12. 12.
    Rawat, S. and Raman, B. A blind watermarking algorithm based on fractional Fourier transform and visual cryptography. Signal Processing, 92(6):1480–1491, 2012.CrossRefGoogle Scholar
  13. 13.
    Tefas, A., Nikolaidis, A., Nikolaidis, N., et al. Statistical analysis of markov chaotic sequences for watermarking applications. In IEEE International Symposium on Circuits and Systems, number 2, pages 57–60, Sydney, NSW, 2001.Google Scholar
  14. 14.
    Tian, J., Bloom, J. A. and Baum, P. G. False positive analysis of correlation ratio watermark detection measure. In IEEE International Conference on Multimedia and Expo, pages 619–622, Beijing, China, 2007.Google Scholar
  15. 15.
    Vatsa, M., Singh, R. and Noore, A. Feature based RDWT watermarking for multimodal biometric system. Image and Vision Computing, 27(3):293–304, 2009.CrossRefGoogle Scholar
  16. 16.
    Wong, P. W. and Memon, N. Secret and public key image watermarking schemes for image authentication and ownership verification. IEEE Transactions on Image Processing, 10(10):1593–1601, 2001.CrossRefzbMATHGoogle Scholar
  17. 17.
    Xiao, J. and Wang, Y. False negative and positive models of dither modulation watermarking. In IEEE Fourth International Conference on Image and Graphics, pages 318–323, Sichuan, 2007.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Himanshu Agarwal
    • 1
    Email author
  • Balasubramanian Raman
    • 2
  • Pradeep K. Atrey
    • 3
  • Mohan Kankanhalli
    • 4
  1. 1.Department of MathematicsJaypee Institute of Information TechnologyNoidaIndia
  2. 2.Indian Institute of Technology RoorkeeRoorkeeIndia
  3. 3.State University of New YorkAlbanyUSA
  4. 4.National University of SingaporeSingaporeSingapore

Personalised recommendations