Video Watermarking in Sparse Domain

  • Ashish M. Kothari
  • Vedvyas Dwivedi
  • Rohit M. Thanki
Part of the Signals and Communication Technology book series (SCT)


This chapter presents video watermarking approach using compressive sensing (CS) theory process in sparse domain. The experimental results of these approaches are also demonstrated in this chapter.


Compressive sensing (CS) Measurements Sparse domain 


  1. 1.
    M. Sheikh and R. Baraniuk, Blind error free detection of transform domain watermarks. IEEE Int. Conf. Image. Proc. 5 Sept 2007Google Scholar
  2. 2.
    F. Tiesheng, L. Guiqiang, D. Chunyi, W. Danhua, A digital image watermarking method based on the theory of compressed sensing. Int. J. Autom. Control Eng. 2(2), 56–61 (2013)Google Scholar
  3. 3.
    A. Sreedhanya, K. Soman, Ensuring security to the compressed sensing data using a Steganographic approach. Bonfring Int. J. Adv. Image Proces. 3(1), 1–7 (2013)CrossRefGoogle Scholar
  4. 4.
    M. Fakhr, Robust watermarking using compressed sensing framework with application to MP3 audio. Int. J. Multimed. Appl. (IJMA) 4(6), 27–43 (2012)Google Scholar
  5. 5.
    M. Raval, M. Joshi, P. Rege and S. Parulkar, Image tampering detection using compressive sensing based watermarking scheme. Proc. MVIP 2011. 2011Google Scholar
  6. 6.
    X. Zhang, Z. Qian, Y. Ren, G. Feng, Watermarking with flexible self-recovery quality based on compressive sensing and compositive reconstruction. IEEE Trans. Inf. Forensic. Secur. 6(4), 1123–1232 (2011)Google Scholar
  7. 7.
    M. Tagliasacchi, G. Valenzise, S. Tubaro, G. Cancelli, M. Barni, A compressive sensing based watermarking scheme for sparse image tampering identification. Proc. ICIP 2009. 1265–1268 (2009)Google Scholar
  8. 8.
    D. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    E. Candes, Compressive Sampling. Proc. Int. Congr. Math. 1–20 (2006)Google Scholar
  10. 10.
    R. Baraniuk, Compressive Sensing. IEEE Signal Process. Mag. 24, 118–124 (2007)CrossRefGoogle Scholar
  11. 11.
    E. Candes and J. Romberg, L1-Magic: recovery of sparse signals via convex programming. 1–19 (2005)Google Scholar
  12. 12.
    J. Tropp, A. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    A. Gilbert, M. Strauss, J. Tropp, R. Vershynin, One sketch for all: Fast algorithms for compressed sensing. 39th ACM Symp. Theory Comput. (STOC). 237–246 (2007)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Ashish M. Kothari
    • 1
  • Vedvyas Dwivedi
    • 2
  • Rohit M. Thanki
    • 2
  1. 1.Atmiya Institute of Technology and ScienceRajkotIndia
  2. 2.C. U. Shah UniversityWadhwan CityIndia

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