A Novel Scene Based Robust Video Watermarking Scheme in DWT Domain Using Extreme Learning Machine

  • Charu Agarwal
  • Anurag Mishra
  • Arpita Sharma
  • Girija Chetty
Part of the Adaptation, Learning, and Optimization book series (ALO, volume 16)


In this chapter, we present a novel fast and robust watermarking scheme for three different standard video in RGB uncompressed AVI format in DWT domain using a newly developed SLFN commonly known as Extreme Learning Machine (ELM). The embedding is carried out by using scene detection. The LL4 sub-band coefficients of frames constitute the dataset to train the ELM in millisecond time. The output of the ELM is used to embed a binary watermark in the video frames using a pre-specified formula. The resultant video exhibits good visual quality. Five different video processing attacks are executed over signed video. The extracted watermarks from the signed and attacked video yield high normalized correlation (NC) values and low Bit Error Rate (BER) values. This indicates successful watermark recovery and the embedding scheme is found to be robust against these common attacks. It is concluded that the proposed watermarking scheme produces best results due to optimized embedding facilitated by fast training of the ELM. The proposed scheme is found to be suitable for developing real time video watermarking applications due to its low time complexity.


Extreme learning machine Video watermarking Uncompressed RGB AVI format Scene detection 


  1. 1.
    F. Hartung, B. Girod, Watermarking of uncompressed and compressed video. Sig. Process. 66(3), 283–301 (1998)Google Scholar
  2. 2.
    S. Biswas, E.M. Petriu, An adaptive compressed MPEG-2 video watermarking scheme. IEEE Trans. Instrum. Measur. 54(5), 1853–1861 (2005)CrossRefGoogle Scholar
  3. 3.
    L. Rajab, T.A. Khatib, A.A. Haj, Video watermarking algorithms using the SVD transform. Eur. J. Sci. Res. 30(3), 389–401 (2009)Google Scholar
  4. 4.
    O.S. Fargallah, Efficient video watermarking based on singular value decomposition in the discrete wavelet transform domain. AEU Int. J. Electron. Commun. 67(3), 189–196 (2013)CrossRefGoogle Scholar
  5. 5.
    C.H. Wu, Y. Zheng, W.H. Ip, C.Y. Chan, K.L. Yung, Z.M. Lu, A Flexible n H.264/AVC compressed video watermarking scheme using particle swarm optimization based dither modulation. Int. J. Electron. Commun. 65, 27–36 (2011)CrossRefGoogle Scholar
  6. 6.
    M. El’Arbi, C.B. Amar, H. Nicolas, Video watermarking based on neural networks, in IEEE International Conference on Multimedia and Expo, pp. 1577–1580, (2006)Google Scholar
  7. 7.
    Y.-Q. Chen, L.-H. Pen, Streaming media watermarking algorithm based on synergetic neural network, in International conference on Wavelet Analysis and Pattern Recognition, pp. 271–275, 2008Google Scholar
  8. 8.
    X. Li, R. Wang, A video watermarking scheme based on 3D-DWT and neural network, in 9th IEEE International Symposium on Multimedia, pp. 110–114, 2007Google Scholar
  9. 9.
    B. Isac, V. Santhi, A study on digital image and video watermarking schemes using neural networks. Int. J. Comput. Appl. 129, 1–6 (2011)Google Scholar
  10. 10.
    N. Leelavathy, E.V. Prasad S. Srinivas Kumar, A scene based video watermarking in discrete multiwavelet domain. Int. J. Multi. Sci. Eng. 37, 12–16 (2012)Google Scholar
  11. 11.
    A. Mishra, A. Goel, R. Singh, G. Chetty, L. Singh, A novel image watermarking scheme using extreme learning machine, in IEEE World Congress on Computational Intelligence, pp. 1–6, 2012Google Scholar
  12. 12.
    M.-B. Lin, G.-B. Huang, P. Saratchandran, N. Sudararajan, Fully complex extreme learning machine. Neurocomputing 68, 306–314 (2005)Google Scholar
  13. 13.
    G.-B. Huang, Q.-Y. Zhu, C.K. Siew, Extreme learning machine. Neurocomputing 70, 489–501 (2006)CrossRefGoogle Scholar
  14. 14.
    G.-B. Huang, Q.-Y. Zhu C.K Siew, Real-time learning capability of neural networks. IEEE Trans. Neural Netw. 174, 863–878 (2006)Google Scholar
  15. 15.
    G.-B. Huang, The Matlab code for ELM is available on
  16. 16.
    D. Serre, Matrices: Theory and Applications (Springer, 2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Charu Agarwal
    • 1
  • Anurag Mishra
    • 2
  • Arpita Sharma
    • 3
  • Girija Chetty
    • 4
  1. 1.DelhiIndia
  2. 2.DelhiIndia
  3. 3.DelhiIndia
  4. 4.CanberraAustralia

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