Full band watermarking in DCT domain with Weibull model

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).

[1]  Gang Wang,et al.  Embedding color image watermark in color image based on two-level DCT , 2015, Signal Image Video Process..

[2]  L. Ghouti,et al.  High-capacity colour image watermarking using multi-dimensional Fourier transforms and semi-random LDPC codes , 2012 .

[3]  N. Balakrishnan,et al.  On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data , 2008 .

[4]  Kai-Sheng Song,et al.  A globally convergent and consistent method for estimating the shape parameter of a generalized Gaussian distribution , 2006, IEEE Transactions on Information Theory.

[5]  C. Das,et al.  A novel blind robust image watermarking in DCT domain using inter-block coefficient correlation , 2014 .

[6]  Minh N. Do,et al.  Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance , 2002, IEEE Trans. Image Process..

[7]  Marc Moonen,et al.  Joint DOA and multi-pitch estimation based on subspace techniques , 2012, EURASIP J. Adv. Signal Process..

[8]  Mahdi Teimouri,et al.  On the Three-Parameter Weibull Distribution Shape Parameter Estimation , 2021, Journal of Data Science.

[9]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[10]  Yongyi Yang,et al.  Locally optimum detection for additive watermarking in the DCT and DWT domains through non-Gaussian distributions , 2013, 2013 18th International Conference on Digital Signal Processing (DSP).

[11]  Luan Dong,et al.  Maximum likelihood watermark detection in absolute domain using Weibull model , 2014, 2014 IEEE REGION 10 SYMPOSIUM.

[12]  Fernando Pérez-González,et al.  DCT-domain watermarking techniques for still images: detector performance analysis and a new structure , 2000, IEEE Trans. Image Process..

[13]  Farrokh Marvasti,et al.  Contourlet-Based Image Watermarking Using Optimum Detector in a Noisy Environment , 2010, IEEE Transactions on Image Processing.

[14]  Wei Liu,et al.  Optimum Detection for Spread-Spectrum Watermarking That Employs Self-Masking , 2007, IEEE Trans. Inf. Forensics Secur..

[15]  Panagiotis Tsakalides,et al.  Hidden messages in heavy-tails: DCT-domain watermark detection using alpha-stable models , 2005, IEEE Transactions on Multimedia.

[16]  Wenjun Zeng,et al.  Optimum Detection for Spread-Spectrum Watermarking That Employs Self-Masking , 2007, IEEE Transactions on Information Forensics and Security.

[17]  Mauro Barni,et al.  A new decoder for the optimum recovery of nonadditive watermarks , 2001, IEEE Trans. Image Process..

[18]  King Ngi Ngan,et al.  Spatial just noticeable distortion profile for image in DCT domain , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[19]  Michael G. Strintzis,et al.  Locally optimum nonlinearities for DCT watermark detection , 2004, IEEE Transactions on Image Processing.

[20]  Muhammad Khurram Khan,et al.  Dynamic weighted discrimination power analysis: A novel approach for face and palmprint recognition in DCT domain , 2010 .

[21]  Z. Jane Wang,et al.  Efficient blind decoders for additive spread spectrum embedding based data hiding , 2012, EURASIP J. Adv. Signal Process..

[22]  D. Rubin,et al.  ML ESTIMATION OF THE t DISTRIBUTION USING EM AND ITS EXTENSIONS, ECM AND ECME , 1999 .

[23]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[24]  Parul Garg,et al.  Multiplicative watermarking of audio in DFT magnitude , 2012, Multimedia Tools and Applications.

[25]  H. Vincent Poor,et al.  An introduction to signal detection and estimation (2nd ed.) , 1994 .

[26]  Thomas S. Huang,et al.  Robust optimum detection of transform domain multiplicative watermarks , 2003, IEEE Trans. Signal Process..

[27]  S. Schuster Parameter estimation for the Cauchy distribution , 2012, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP).

[28]  H. Garg,et al.  Maximum Likelihood Detection in Image Watermarking Using Generalized Gamma Model , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[29]  Ling-Yuan Hsu,et al.  Blind image watermarking via exploitation of inter-block prediction and visibility threshold in DCT domain , 2015, J. Vis. Commun. Image Represent..

[30]  Weisi Lin,et al.  A Video Saliency Detection Model in Compressed Domain , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[31]  Thomas S. Huang,et al.  An additive approach to transform-domain information hiding and optimum detection structure , 2001, IEEE Trans. Multim..

[32]  Marco Botta,et al.  SS-SVD: Spread spectrum data hiding scheme based on Singular Value Decomposition , 2015, 2015 International Symposium on Consumer Electronics (ISCE).

[33]  Yi Shi,et al.  Embedding image watermarks in dc components , 2000, IEEE Trans. Circuits Syst. Video Technol..