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UDTCWT difference domain statistical decoder using vector-based Weibull PDF

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Abstract

Invisibility, data payload and robustness are widely regarded as the three main attributes that are critical to any digital image watermarking schemes. They restrict each other, so it is a research focus to strike a favorable balance between them. Multiplicative watermarking based on statistical models is an effective method to achieve the trade-off between these three demands. However, most of the existing approaches until now focus on employing transform coefficients, which are usually poor in resistance to multiple attacks. We proposed a new digital image watermarking system in the difference domain of Undecimated Dual Tree Complex Wavelet Transform (UDTCWT) in this article, in which the UDTCWT difference coefficients are used for hiding the watermark information and constructing the watermark decoder. In the process of embedding the watermark, the watermark message is hidden in the UDTCWT difference domain of the carrier image using a multiplicative manner so as to achieve better imperceptibility and robustness. In watermark decoding, we first prove that the vector based Weibull distribution can suitably fit the distribution of the UDTCWT difference coefficients. And then, we constructed a blind statistical digital image watermark decoder in UDTCWT difference coefficients, combining the vector based Weibull distribution with the locally most powerful (LMP) rule. Experimental results on some standard test images demonstrate the efficacy and superiority of the proposed approach.

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References

  1. Ahmaderaghi B, Kurugollu F, Rincon J (2018) Blind image watermark detection algorithm based on discrete shearlet transform using statistical decision theory. IEEE Trans Computational Imaging 4(1):46–59

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  3. Amini M, Ahmad MO, Swamy MNS (2018) A robust multibit multiplicative watermark decoder using vector-based hidden Markov model in wavelet domain. IEEE Trans Circuits Syst Video Technol 28(2):402–413

    Article  Google Scholar 

  4. Amini M, Sadreazami H, Ahmad MO, Swamy MNS (2019) A channel-dependent statistical watermark detector for color images. IEEE Trans on Multimedia 21(1):65–73

    Article  Google Scholar 

  5. Amirmazlaghani M (2016) Additive watermark detection in the wavelet domain using 2D-GARCH model. Inf Sci 370:1–17

    Article  MathSciNet  Google Scholar 

  6. Amirmazlaghani M (2019) Heteroscedastic watermark detector in the contourlet domain. IET Comput Vis 13(3):249–260

    Article  MathSciNet  Google Scholar 

  7. Bhinder P, Jindal N, Singh K (2020) An improved robust image-adaptive watermarking with two watermarks using statistical decoder. Multimed Tools Appl 79(2):183–217

    Article  Google Scholar 

  8. Bi H, Liu Y, Wu M (2016) NSCT domain additive watermark detection using RAO hypothesis test and Cauchy distribution. Math Probl Eng 2016:1–18

    MATH  Google Scholar 

  9. Bian Y, Liang S (2013) Locally optimal detection of image watermarks in the wavelet domain using Bessel K form distribution. IEEE Trans Image Process 22(6):2372–2384

    Article  MathSciNet  MATH  Google Scholar 

  10. Briassouli A, Tsakalides P (2005) Hidden messages in heavy-tails: DCT-domain watermark detection using alpha-stable models. IEEE Trans on Multimedia 7(4):700–715

    Article  Google Scholar 

  11. Dong L, Yan Q, Lv Y, Deng S (2016) Full band watermarking in DCT domain with Weibull model. Multimed Tools Appl 76(2):1–18

    Google Scholar 

  12. Dutta MK, Gupta P, Pathak VK (2009) Blind watermarking in audio signals using biometric features in wavelet domain. TENCON 2009-2009 IEEE Region 10 Conference. IEEE

  13. Etemad S, Amirmazlaghani M (2018) A new multiplicative watermark detector in the contourlet domain using t location-scale distribution. Pattern Recogn 77:99–112

    Article  Google Scholar 

  14. Evsutin O, Dzhanashia K (2022) Watermarking schemes for digital images: robustness overview. Signal Process Image Commun 100:116523

    Article  Google Scholar 

  15. Fisher NI, Switzer P (1985) Chi-plots for assessing dependence. Biometrica 72:253–265

    Article  MathSciNet  MATH  Google Scholar 

  16. Hill PR, Anantrasirichai N, Achim A, Bull DR (2015) Undecimated dual-tree complex wavelet transforms. Signal Process Image Commun 35:61–70

    Article  Google Scholar 

  17. Kalantari NK, Ahadi SM, Vafadust M (2010) A robust image watermarking in the Ridgelet domain using universally optimum decoder. IEEE Trans Circuits Syst Video Technol 20(3):396–406

    Article  Google Scholar 

  18. Li Y, Wang H, Barni M (2021) A survey of deep neural network watermarking techniques. Neurocomputing 461:171–193

    Article  Google Scholar 

  19. Liu J, Rao Y (2019) Optimization-based image watermarking algorithm using a maximum-likelihood decoding scheme in the complex wavelet domain. KSII Trans Internet Inf Syst 13(1):452–472

    Google Scholar 

  20. Niu P, Shen X, Wei T, Yang H, Wang X (2020) Blind image watermark decoder in UDTCWT domain using weibull mixtures-based vector HMT. IEEE Access 8:46624–46641

    Article  Google Scholar 

  21. Rabizadeh M, Amirmazlaghani M, Ahmadian-Attari M (2016) A new detector for contourlet domain multiplicative image watermarking using Bessel K form distribution. J Vis Commun Image Represent 40:324–334

    Article  Google Scholar 

  22. Rahman SMM, Ahmad MO, Swamy MNS (2009) A new statistical detector for DWT-based additive image watermarking using the Gauss-Hermite expansion. IEEE Trans Circuits Syst Video Technol 18(8):1782–1796

    MathSciNet  MATH  Google Scholar 

  23. Riad R, Jennane R, Brahim A, Janvier T (2018) Texture analysis using complex wavelet decomposition for knee osteoarthritis detection: data from the osteoarthritis initiative. Comput Electr Eng 68:181–191

    Article  Google Scholar 

  24. Sadreazami H, Amini M (2019) A robust image watermarking scheme using local statistical distribution in the Contourlet domain. IEEE Trans Circuits Syst II Express Briefs 66(1):151–155

    Google Scholar 

  25. Sadreazami H, Ahmad MO, Swamy MNS (2014) A study of multiplicative watermark detection in the contourlet domain using alpha-stable distributions. IEEE Trans Image Process 23(10):4348–4360

    Article  MathSciNet  MATH  Google Scholar 

  26. Sadreazami H, Ahmad MO, Swamy MNS (2015) A robust multiplicative watermark detector for color images in sparse domain. IEEE Trans Circuits Syst II Express Briefs 62(12):1159–1163

    Google Scholar 

  27. Sadreazami H, Ahmad MO, Swamy MNS (2016) Multiplicative watermark decoder in contourlet domain using the normal inverse Gaussian distribution. IEEE Trans on Multimedia 18(2):196–207

    Article  Google Scholar 

  28. Sparacino G, Tombolato C, Cobelli C (2000) Maximum-likelihood versus maximum a posteriori parameter estimation of physiological system models: the c-peptide impulse response case study. IEEE Trans Biomed Eng 47(6):801–811

    Article  Google Scholar 

  29. Wang X, Wen T, Wang L, Niu P, Yang H (2020) Contourlet domain locally optimum image watermark decoder using Cauchy mixtures based vector HMT model. Signal Process Image Commun 88:115972

    Article  Google Scholar 

  30. Wang XY, Shen X, Niu PP, Yang HY (2022) BGGMM-HMT based locally optimum image watermark detector in high-order NSST difference domain. J Vis Commun Image Represent 83:103450

    Article  Google Scholar 

  31. Yang HY, Liang LL, Zhang C, Wang XB, Niu PP, Wang XY (2019) Weibull statistical modeling for textured image retrieval using nonsubsampled contourlet transform. Soft Comput 23(13):4749–4764

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported partially by the National Natural Science Foundation of China (Nos. 61472171 & 61701212), Scientific Research Project of Liaoning Provincial Education Department (No. LJKZ0985), and Natural Science Foundation of Liaoning Province (No. 2019-ZD-0468).

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, People’s Republic of China

    Xiangyang Wang, Yixuan Shen, Yu Dai, Jing Tian, Panpan Niu & Hongying Yang

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  1. Xiangyang Wang
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  2. Yixuan Shen
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  3. Yu Dai
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  4. Jing Tian
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Correspondence to Xiangyang Wang or Hongying Yang.

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Wang, X., Shen, Y., Dai, Y. et al. UDTCWT difference domain statistical decoder using vector-based Weibull PDF. Multimed Tools Appl 81, 43037–43061 (2022). https://doi.org/10.1007/s11042-022-13229-9

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  • DOI: https://doi.org/10.1007/s11042-022-13229-9

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