Skip to main content
SpringerLink
Log in

Rapid blockwise multi-resolution clustering of facial images for intelligent watermarking

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Population-based evolutionary computation (EC) is widely used to optimize embedding parameters in intelligent watermarking systems. Candidate solutions generated with these techniques allow finding optimal embedding parameters of all blocks of a cover image. However, using EC techniques for full optimization of a stream of high-resolution grayscale face images is very costly. In this paper, a blockwise multi-resolution clustering (BMRC) framework is proposed to reduce this cost. During training phase, solutions obtained from multi-objective optimization of reference face images are stored in an associative memory. During generalization operations, embedding parameters of an input image are determined by searching for previously stored solutions of similar sub-problems in memory, thereby eliminating the need for full optimization for the whole face image. Solutions for sub-problems correspond to the most common embedding parameters for a cluster of similar blocks in the texture feature space. BMRC identifies candidate block clusters used for embedding watermark bits using the robustness score metric. It measures the texture complexity of image block clusters and can thereby handle watermarks of different lengths. The proposed framework implements a multi-hypothesis approach by storing the optimization solutions according to different clustering resolutions and selecting the optimal resolution at the end of the watermarking process. Experimental results on the PUT face image database show a significant reduction in complexity up to 95.5 % reduction in fitness evaluations compared with reference methods for a stream of 198 face images.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Baluja, S.: Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Tech. rep, CMU (1994)

  2. Bureerat, S., Sriworamas, K.: Population-based incremental learning for multiobjective optimization. Soft Comput. Ind. Appl. 39, 223–232 (2007)

    Google Scholar 

  3. Deb., K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

  4. Diaz, D.S., Romay, M.G.: Introducing a watermarking with a multi-objective genetic algorithm. In: Genetic and Evolutionary Computation Conference (GECCO 2005), pp. 2219–2220 (2005)

  5. Haouzia, A., Noumeir, R.: Methods for image authentication: a survey. MultiMed Tools Appl. 39, 1–46 (2008)

    Google Scholar 

  6. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)

    Article  Google Scholar 

  7. Kasinski, A., Florek, A., Schmidt, A.: The put face database. Image Process. Commun. 13(3–4), 59–64 (2008)

    Google Scholar 

  8. Lee, Z.J., Lin, S.W., Su, S.F., Lin, C.Y.: A hybrid watermarking technique applied to digital images. Appl. Soft Comput. 8, 798–808 (2008)

    Google Scholar 

  9. Licks, V., Jordan, R.: Geometric attacks on image watermarking systems, pp. 68–78. IEEE Multimedia, IEEE Computer Society (2005)

  10. Nie, F., Zeng, Z., Tsang, I.W., Xu, D., Zhang, C.: Spectral embedded clustering: A framework for in-sample and out-of-sample spectral clustering. IEEE Trans. on Neural Networks (T-NN) 22(11), 1796–1808 (2011)

    Google Scholar 

  11. Pereira, S., Voloshynovskiy, S., Madueno, M., Marchand-Maillet, S., Pun, T.: Second generation benchmarking and application oriented, evaluation. pp. 340–353 (2001)

  12. Rabil, B.S., Sabourin, R., Granger, E.: Intelligent watermarking with multi-objective population based incremental learning. In: IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing(IIH-MSP 2010), pp. 131–134. Darmstadt, Germany (2010)

  13. Rabil, B.S., Sabourin, R., Granger, E.: Impact of watermarking attacks on biometric verification systems in intelligent bio-watermarking systems. In: IEEE Workshop on Computational Intelligence in Biometrics and Identity Management 2011. IEEE Symposium Series On Computational Intelligence 2011, pp. 13–20. Paris, France (2011)

  14. Rabil, B.S., Sabourin, R., Granger, E.: Watermarking stack of grayscale face images as dynamic multi-objective optimization problem. In: International Conference on Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry, pp. 63–77. New York/USA (2011)

  15. Shieh, C.S., Huang, H.C., Wang, F.H., Pan, J.S.: Genetic watermarking based on transform-domain techniques. Pattern Recogn. 37, 555–565 (2004)

    Article  Google Scholar 

  16. Sorwar, G., Abraham, A.: Dct based texture classification using a soft computing approach. Malays. J. Comput. Sci. 17(1), 13–23 (2004)

    Google Scholar 

  17. Vellasques, E., Granger, E., Sabourin, R.: Intelligent Watermarking Systems: a Survey, Handbook of Pattern Recognition and Computer Vision, 4th edn., pp. 1–40 (2010)

  18. Vellasques, E., Sabourin, R., Granger, E.: A high throughput system for intelligent watermarking of bi-tonal images. Appl. Soft Comput. 11(8), 5215–5229 (2011)

    Google Scholar 

  19. Vellasques, E., Sabourin, R., Granger, E.: Gaussian mixture modeling for dynamic particle swarm optimization of recurrent problems. In: Genetic and Evolutionary Computation Conference (GECCO 2012), pp. 73–80. Philadelphia, USA (2012)

  20. Voloshynovsky, S., Herrigel, A., Baum, N.: A stochastic approach to content adaptive digital image watermarking. In: Proceedings of the 3rd International Workshop on Information Hiding 1999, pp. 211–236. Dresden, Germany (1999)

  21. Wang, Z., Sun, X., Zhang, D.: A novel watermarking scheme based on pso algorithm. In: Proceedings of the Life System Modeling and Simulation 2007, (LSMS’07). International Conference on Bio-Inspired Computational Intelligence and Applications, pp. 307–314 (2007)

  22. Wu, M.: Multimedia Data Hiding. Ph.D. thesis, Princeton University (2001)

  23. Yang, Y., Xu, D., Nie, F., Yan, S., Zhuang, Y.: Image clustering using local discriminant models and global integration. IEEE Trans. Image Process. (T-IP) 10, 2761–2773 (2010)

    Google Scholar 

  24. Yu, F., Oyana, D., Hou, W.C., Wainer, M.: Approximate clustering on data streams using discrete cosine transform. J. Inform. Process. Syst. 6(1), 67–78 (2010)

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by the Natural Sciences and Engineering Research Council of Canada and BancTec Inc.

Author information

Authors and Affiliations

  1. Laboratoire d’Imagerie, de Vision, et d’Intelligence Artificielle, École de Technologie Supérieure, Université du Québec, 1100 Notre-Dame Ouest, Montreal, QC, H3C 1K3, Canada

    Bassem S. Rabil, Robert Sabourin & Eric Granger

Authors
  1. Bassem S. Rabil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert Sabourin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eric Granger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bassem S. Rabil.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rabil, B.S., Sabourin, R. & Granger, E. Rapid blockwise multi-resolution clustering of facial images for intelligent watermarking. Machine Vision and Applications 25, 277–300 (2014). https://doi.org/10.1007/s00138-013-0493-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-013-0493-1

Keywords

Search

Navigation