Skip to main content
SpringerLink
Log in

Full band watermarking in DCT domain with Weibull model

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

Abstract

In the framework of maximum-likelihood detection for image watermarking schemes, the conventional Generalized Gaussian Distribution (GGD), Cauchy and Student’s t distributions often fail to model the pulse-like distributions, such as Discrete Cosine Transform (DCT) coefficient distribution. Meanwhile DCT DC coefficients are often neglected in the image watermarking schemes. In this paper an improved full band image watermarking algorithm with utilization of Weibull distribution modeling the DCT AC and DC coefficients is proposed. Experiments indicate that compared with other popluar distributions such as the GGD, the Weibull model gives a closer fit on the distribution of AC coefficients in absolute domain with a smaller Kullback-Leibler (KL) divergence and lower Mean Square Error (MSE). The watermarking scheme with Weibull modeling the DCT AC coefficients (Weibull-AC) exhibits strong robustness under the attack of scaling and median filtering. The watermarking scheme with Weibull modeling the DCT DC coefficients (Weibull-DC) yields a better detection accuracy for bright and more detailed images. Combining the above two advantages, the proposed Weibull based full band watermarking in DCT domain (Weibull-FB) further improves its robustness under the attack of JPEG compression and achieves 10.47 % overall increment in the detection accuracy compared with the baseline system while maintaining good invisibility in the view of structural similarity (SSIM).

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

Similar content being viewed by others

References

  1. Achatz RJ, Cotton MG, Dalke RA (2004) Estimating and graphing the amplitude probability distribution function of complex-baseband signals. US DoC, NTIA/ITS, IEEE 802. 15 Working Group for Wireless Personal Area Networks

  2. Akhaee MA, Sahraeian SME, Marvasti F (2010) Contourlet-based image watermarking using optimum detector in a noisy environment. IEEE T Image Process 19(4):967–980. doi:10.1109/TIP.2009.2038774

    Article  MathSciNet  MATH  Google Scholar 

  3. Balakrishnan N, Kateri M (2008) On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data. Stat Probab Lett 78(17):2971–2975. doi:10.1016/j.spl.2008.05.019

    Article  MathSciNet  MATH  Google Scholar 

  4. Barni M, Bartolini F, De Rosa A, Piva A (2001) A new decoder for the optimum recovery of nonadditive watermarks. IEEE T Image Process 10(5):755–766. doi:10.1109/83.918568

    Article  Google Scholar 

  5. Briassouli A, Strintzis MG (2004) Locally optimum nonlinearities for DCT watermark detection. IEEE T Image Process 13(12):1604–1617. doi:10.1109/TIP.2004.837516

    Article  Google Scholar 

  6. Briassouli A, Tsakalides P, Stouraitis A (2005) Hidden messages in heavy-tails: DCT-domain watermark detection using alpha-stable models. IEEE Multimedia 7(4):700–715. doi:10.1109/TMM.2005.850970

    Article  Google Scholar 

  7. Cheng Q, Huang TS (2001) An additive approach to transform-domain information hiding and optimum detection structure. IEEE Multimedia 3(3):273–284. doi:10.1109/6046.944472

    Article  Google Scholar 

  8. Cheng Q, Huang TS (2003) Robust optimum detection of transform domain multiplicative watermarks. IEEE T Signal Process 51(4):906–924. doi:10.1109/TSP.2003.809374

    Article  MathSciNet  MATH  Google Scholar 

  9. Das C, Panigrahi S, Sharma VK, Mahapatra KK (2014) A novel blind robust image watermarking in DCT domain using inter-block coefficient correlation. AEU-Int J Electron C 68(3):244–253. doi:10.1016/j.aeue.2013.08.018

    Article  Google Scholar 

  10. Do MN, Vetterli M (2002) Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE T Image Process 11(2):146–158. doi:10.1109/83.982822

    Article  MathSciNet  Google Scholar 

  11. Dong L, Yan Q, Liu M, Pan YX (2014) Maximum likelihood watermark detection in absolute domain using Weibull model. In: Proceedings of IEEE Region 10 Technical Symposium 2014, Kuala Lumpur, Malaysia, 14–16 April, pp 196–199. doi:10.1109/TENCONSpring.2014.6863024

  12. Fang YM, Lin WS, Chen ZZ, Tsai CM, Lin CW (2014) A video saliency detection model in compressed domain. IEEE T Circ Syst Vid 24(1):27–38. doi:10.1109/TCSVT.2013.2273613

    Article  Google Scholar 

  13. Ghouti L, Andalusi AM (2012) High-capacity colour image watermarking using multi-dimensional Fourier transforms and semi-random LDPC codes. In: Proceedings of IET Conference on Image Processing 2012, London, UK, 3–4 July, pp 1–5. doi:10.1049/cp.2012.0456

  14. Hernandez JR, Amado M, Perez-Gonzalez F (2000) DCT-domain watermarking techniques for still images: detector performance analysis and a new structure. IEEE T Image Process 9(1):55–68. doi:10.1109/83.817598

    Article  Google Scholar 

  15. Hsu LY, Hu HT (2015) Blind image watermarking via exploitation of inter-block prediction and visibility threshold in DCT domain. J J Vis Commum Image R 32:130–143. doi:10.1016/j.jvcir.2015.07.017

    Article  Google Scholar 

  16. Huang JW, Shi YQ, Shi Y (2000) Embedding image watermarks in DC components. IEEE T Circ Syst Vid 10(6):974–979. doi:10.1109/76.867936

    Article  Google Scholar 

  17. Leng L, Zhang J, Chen XL, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis: A novel approach for face and palmprint recognition in DCT domain. Int J Phys Sci 5(17):2543–2554

    Google Scholar 

  18. Liu CH, Rubin DB (1995) ML estimation of the t distribution using EM and its extensions, ECM and ECME. Stat Sinica 5(1):19–39

    MathSciNet  MATH  Google Scholar 

  19. Liu W, Dong L, Zeng W (2007) Optimum detection for spread-spectrum watermarking that employs self-masking. IEEE T Inf Foren Sec 2(4):645–654. doi:10.1109/TIFS.2007.908226

    Article  Google Scholar 

  20. Mairgiotis A, Kondi L, Yang YY (2013) Locally optimum detection for additive watermarking in the DCT and DWT domains through non-Gaussian distributions. In: Proceedings of the 18th International Conference on Digital Signal Processing, Fira, Greek, 1–3 July 2013, pp 1–6. doi:10.1109/ICDSP.2013.6622794

  21. Ng TM, Garg HK (2005) Maximum likelihood detection in image watermarking using generalized gamma model. In: Proceedings of the 39th Asilomar Conference on Signals, Systems and Computers, 28 Oct-1 Nov 2005, pp 1680–1684. doi:10.1109/ACSSC.2005.1600055

  22. Poor HV (1994) An introduction to signal detection and estimation. Springer, New York, pp. 51–53. doi:10.1007/978-1-4757-2341-0

    Book  MATH  Google Scholar 

  23. Schuster S (2012) Parameter estimation for the Cauchy distribution. In: Proceedings of the 19th International Conference on Systems, Signals and Image Processing, Vienna, Austria, 11–13 April 2012, pp 350–353

  24. Singh J, Garg P, De A (2014) Multiplicative watermarking of audio in DFT magnitude. Multimedia Tools Appl 71(3):1431–1453. doi:10.1007/s11042-012-1282-y

    Article  Google Scholar 

  25. SIPI Image Database. University of Southern California. http://sipi.usc.edu/database/. Accessed 14 April 2015

  26. Song KS (2006) A globally convergent and consistent method for estimating the shape parameter of a generalized Gaussian distribution. IEEE T Inform Theory 52(2):510–527. doi:10.1109/TIT.2005.860423

    Article  MathSciNet  MATH  Google Scholar 

  27. Su QT, Wang G, Jia SL, Zhang XF, Liu QM, Liu XX (2015) Embedding color image watermark in color image based on two-level DCT. SIViP 9(5):991–1007. doi:10.1007/s11760-013-0534-2

    Article  Google Scholar 

  28. Teimouri M, Arjun KG (2013) On the three-parameter Weibull distribution shape parameter estimation. J Data Sci 11(3):403–414

    MathSciNet  Google Scholar 

  29. Valizadeh A, Wang ZJ (2012) Efficient blind decoders for additive spread spectrum embedding based data hiding. EURASIP J Adv Sig Process 2012(1):1–21. doi:10.1186/1687-6180-2012-88

    Article  Google Scholar 

  30. Victor P, Cavagnino D, Botta M (2015) SS-SVD: Spread spectrum data hiding scheme based on Singular Value Decomposition. In: Proceedings of the IEEE International Symposium on Consumer Electronics (ISCE), Madrid, 24–26 June 2015, pp 1–2. doi:10.1109/ISCE.2015.7177769

  31. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE T Image Process 13(4):600–612. doi:10.1109/TIP.2003.819861

    Article  Google Scholar 

  32. Wei Z, Ngan KN (2008) Spatial just noticeable distortion profile for image in DCT domain. In: Proceedings of the International Conference on Multimedia and Expo, Hannover, June, pp 925–928. doi:10.1109/ICME.2008.4607587

Download references

Acknowledgments

This research was supported by National Natural Science Foundation of China (No.61170297, 61301218) and Qing Lan Project of Jiangsu Province of China.

Author information

Authors and Affiliations

  1. College of Computer and Information, Hohai University, No. 8 Focheng West Road, Nanjing, 211100, China

    Luan Dong, Qin Yan, Yong Lv & Shuyu Deng

  2. College of Computer and Information Engineering, Xinjiang Agriculture University, No.311 Nongda East Road, Urumqi, 830052, China

    Luan Dong

Authors
  1. Luan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qin Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuyu Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qin Yan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, L., Yan, Q., Lv, Y. et al. Full band watermarking in DCT domain with Weibull model. Multimed Tools Appl 76, 1983–2000 (2017). https://doi.org/10.1007/s11042-015-3115-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-3115-2

Keywords

Search

Navigation