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A robust multiplicative watermarking technique for digital images in curvelet domain using normal inverse Gaussian distribution

  • 1209: Recent Advances on Social Media Analytics and Multimedia Systems: Issues and Challenges
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Abstract

The illegal duplication of digital copies became easier because of increased communication speed and rapid growth of internetworked multimedia systems. So it is required to authenticate the legal owner of the digital data and protect the copyrights of the content owner. But the original image requirement during the verification of the owner becomes an overhead as it requires more storage. Instead of that, the watermark extraction without original data will be helpful which is also blind watermarking as the storage complexity is reduced. This article proposes a novel blind multiplicative watermarking (WM) system in the curvelet domain for copyright protection. The proper distribution of the curvelet coefficients has known by modeling the statistics of the curvelet coefficients of a digital image which is a heavy-tailed probability distribution function. It has shown that the normal inverse Gaussian (NIG) distribution suitably fits the empirical distribution. A secure watermark is used for watermarking, a combination of the pseudo-random sequence and the unique information of the owner in the proposed article. The design of the watermark extractor has been realized using NIG for the curvelet coefficients of digital images by using the above property. The watermark is decoded from NIG curvelet coefficients with expectation-maximization (EM) algorithm using maximum likelihood estimation (MLE). The watermark has been decoded using closed-form expressions in both the noise and noiseless environments. The experimental results on standard datasets (BOWS and SIPI) show that the proposed scheme provides approximately 20% improvement in PSNR and 50% reduction in bit error rate (BER) compared to the listed method in the article. The proposed technique shows high robustness against attacks such as crop**, rotation, median filtering, salt and pepper noise, Gaussian noise, average filtering, and gamma correction.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Melakkottaiyur, Chennai, 600127, Tamilnadu, India

    Ayesha Shaik

  2. Computer Science and Engineering, Indian Institute of Information technology, Design and Manufacturing, Kancheepuram, Chennai, 600127, Tamilnadu, India

    Masilamani V

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  1. Ayesha Shaik
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  2. Masilamani V
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Dr.Ayesha shaik contributed for methodology, visaulization, and draft preparation. Dr.Masilamani V contributed for methodology, and reviewing the draft.

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Correspondence to Ayesha Shaik.

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Shaik, A., V, M. A robust multiplicative watermarking technique for digital images in curvelet domain using normal inverse Gaussian distribution. Multimed Tools Appl 82, 9223–9241 (2023). https://doi.org/10.1007/s11042-022-14137-8

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