An improved robust image-adaptive watermarking with two watermarks using statistical decoder

  • Preeti Bhinder
  • Neeru Jindal
  • Kulbir SinghEmail author


This paper presents an improved image-adaptive watermarking technique. Two image watermarks are embedded in the high entropy 8 × 8 blocks of the host image. DWT is applied on these blocks using the principle of sub band coding. This decomposes the high entropy blocks into four sub band coefficients, wherein the approximation and vertical frequency coefficients are modeled using Gaussian (or Normal) distribution. The two watermarks are inserted in the host image using Adjustable Strength Factor (ASF). It is calculated adaptively using the fourth statistical moment known as kurtosis. A limited side information is also transmitted along with the watermarked image. This side information consists of high entropy block positions and Gaussian distribution parameters. To extract both watermarks from the received watermarked image, the high entropy block positions sent in the side information help in applying DWT to calculate the approximation and vertical frequency coefficients. Gaussian (or Normal) distribution is similarly used for modeling and calculating the distribution parameters. This helps the Maximum Likelihood (ML) decoder to recover the watermarks successfully using a statistical approach. Two important contributions are presented in this paper. Firstly, adjustable kurtosis values are used which improves the capacity and robustness of the proposed technique. Secondly, the proposed work is implemented on medical applications and gives better performance as compared to the existing methods. Further, the efficiency of the proposed work is evaluated by better simulation results using PSNR, NCC, SSIM and GMSD under different attacks. The technique is highly robust as watermarks survive under different attacks. This increases security and ensures copyright protection.


Multiple image-adaptive watermarking Kurtosis Statistical modeling Gradient mean structural deviation (GMSD) Maximum likelihood (ML) decoder 



The authors would like to thank the anonymous reviewers for their valuable comments which have helped to improve the quality of the paper.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringThapar Institute of Engineering and TechnologyPatialaIndia
  2. 2.Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjabIndia

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