Zero Digital Images Watermarking Method Based on Cellular Neural Network and Contourlet Transform

  • Jie Zhao
  • Yawen Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 206)


Watermark embedding introduces inevitably some perceptible quality degradation of the host image. Another problem is the inherent conflict between imperceptibility and robustness. However, zero-watermarking technique can extract some essential characteristics from the host image and use them for watermark registration and detection. The original image was decomposed into series of multiscale and directional subimages after contourlet transform. The low-frequency subimage and watermark image are inputs of the cellular neural network (CNN), and the zero-watermarking registration image is the output. The geometric moments and log-polar mapping are employed to against scaling and rotation attacks. To investigate and improve the security and robustness, the original watermark and registration image are scrambled or encrypted. The proposed method is simple for hardware realization. Experimental results show that it is robust to many common image operations.


Zero watermarking Cellular neural networks Contourlet transform 



Supported by the Science and Technology Foundation Project of Shangluo University (Grant No. 09SKY032, 10SKY1007).


  1. 1.
    Schyndel RG, Tirkel AZ (1994) In: Proceedings of the international conference on image processing, vol 12. Texas, pp 13–16Google Scholar
  2. 2.
    Bender W, Gruhl D, Morimoto N, Lu A (1996) IBM Syst J 35(3&4):313–319CrossRefGoogle Scholar
  3. 3.
    Luo T, Yu M, Jiang G, Wu A, Shao F, Peng Z (2012) Future wireless networks and information systems, LNEE 143:297–307Google Scholar
  4. 4.
    Lan Meng, Hongying Yang, Xiangyang Wang (2008) J Chin Comput Syst 29(11):2153–2163Google Scholar
  5. 5.
    Zhang D, Wu B, Sun J, Huang H (2009) In: Proceedings of the international congress on image and signal processing, vol 9. Tianjin, China, pp 17–19Google Scholar
  6. 6.
    Joseph JK, Ruanaidh O, Pun T (1998) Signal Process 66(3):303–309CrossRefMATHGoogle Scholar
  7. 7.
    Cai Lian, Sidan Du, Duntang Gao (2005) J Electron 22(5):300Google Scholar
  8. 8.
    Quan Wen, Tanfeng Sun, Shuxun Wang (2003) Acta Electron Sin 31(2):214–220Google Scholar
  9. 9.
    Jianhu Ma, Jiaxing He (2007) J Image Graph 12(4):582–587Google Scholar
  10. 10.
    Do MN, Vetterli M (2002) In: Proceedings of the IEEE international conference on image processing, vol 1. New York, pp 22–25Google Scholar
  11. 11.
    Chua LO, Yang L (1988) IEEE Trans Circ Syst 35(10):1257–1264CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Physics and Electronic Information EngineeringShangluo UniversityShangluoChina

Personalised recommendations