Skip to main content
Springer Nature Link
Log in

Fractal and neural networks based watermark identification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Transform techniques generally are more robust than spatial techniques for watermark embedding. In this paper, a color image watermarking algorithm based on fractal and neural networks in Discrete Cosine Transform (DCT) domain is proposed. We apply fractal image coding technique to obtain the characteristic data of a gray-level image watermark signal and encrypt the characteristic data by a symmetric encryption before they are embedded. We then use neural networks and Human Visual System (HVS) to embed the watermark in the DCT domain. A Just Noticeable Difference (JND) threshold controller is designed to ensure the strength of the embedded data adapting to the host image itself entirely. Aiming at misjudging problem of the extracting process, maximum membership principle criterion is selected for identifying the watermark. And the CIELab color space is chosen to guarantee the stability of the results. The simulation results show that the algorithm is robust for common digital image processing methods as attacks and that the quality of the image is retained.

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

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
Fig. 16

Similar content being viewed by others

References

  1. Barni M, Bartolini F (2004) Watermarking systems engineering: enabling digital assets security and other application. Marcel Dekker, New York

    Google Scholar 

  2. Barni M, Bartolini F, Cappellini V, Piva A (1998) A DCT domain system for robust image watermarking. Signal Processing 66(3):357–372

    Article  MATH  Google Scholar 

  3. Cox IJ, Doerr G, Furon T (2006) Watermarking is not cryptography. Lecture notes in computer science, vol 4283. Springer, Berlin, pp 1–15

    Google Scholar 

  4. Cox I, Miller M, Bloom J (2002) Digital watermarking. Morgan Kaufman, San Francisco

    Google Scholar 

  5. Fu X, Wang L (2005) Data dimensionality reduction with application to improving classification performance and explaining concepts of data sets. International Journal of Business Intelligence and Data Mining 1(1):65–87

    Article  Google Scholar 

  6. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey

    MATH  Google Scholar 

  7. Jacquin AE (1992) An image coding based on a fractal theory of iterated contractive image transformations. IEEE Trans Image Process 1(1):18–30

    Article  Google Scholar 

  8. Kang X, Huang J, Shi YQ, Lin Y (2003) A DWT-DFT composite watermarking scheme robust to both affine transform and JPEG compression. IEEE Trans Circuits Syst Video Technol 13(8):776–786

    Article  Google Scholar 

  9. Khan A, Tahir SF, Majid A, Choi T-S (2008) Machine learning based adaptive watermark decoding in view of anticipated attack. Pattern Recogn 41:2594–2610

    Article  MATH  Google Scholar 

  10. Lee CH, Lee YK (1999) An adaptive digital image watermarking technique for copyright protection. IEEE Trans Consum Electron 45(4):1005–1015

    Article  Google Scholar 

  11. Nikolaidis N, Pitas I (1998) Robust image watermarking in the spatial domain. Signal Processing 66(3):385–403

    Article  MATH  Google Scholar 

  12. Podilchuk CI, Zeng W (1998) Image adaptive watermarking using visual models. IEEE J Sel Areas Commun 16(4):525–539

    Article  Google Scholar 

  13. Tao B, Dickinson B (1997) Adaptive watermarking in the DCT domain. ICCASP’97, Munich, pp 2985–2988

    Google Scholar 

  14. Wang YW, Doherty JF, Van Dyck RE (2002) A wavelet-based watermarking algorithm for ownership verification of digital image. IEEE Trans Image Process 11(2):77–88

    Article  Google Scholar 

  15. Wang LP, Fu XJ (2005) Data mining with computational intelligence. Springer, Berlin

    MATH  Google Scholar 

  16. Watson AB (1993) DCT quantization matrices visually optimized for individual images, vol 1913. Proceeding of SPIE conference on human vision, Visual processing and digital display, London, pp 202–216

Download references

Acknowledgment

This research is supported in part by the National High Technology Research and Development Program of China under Grant 2007AA01Z324, in part by Natural Science Foundation of Shaan Xi Educational Committee under Grant 08JK319. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the foundations.

Author information

Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China

    Li Mao, Yang-Yu Fan & Guo-Yun Lv

  2. School of Information and Control Engineering, Xi’AN University of Architecture and Technology, Xi’an, China

    Li Mao & Hui-Qin Wang

Authors
  1. Li Mao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yang-Yu Fan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Hui-Qin Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Guo-Yun Lv
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Li Mao.

Additional information

This work is partially supported by the National High Technology Research and Development Program of China under Grant 2007AA01Z324, the Natural Science Foundation of Shaan Xi Educational Committee under Grant 08JK319.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mao, L., Fan, YY., Wang, HQ. et al. Fractal and neural networks based watermark identification. Multimed Tools Appl 52, 201–219 (2011). https://doi.org/10.1007/s11042-010-0467-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-010-0467-5

Keywords