Blind Image Watermark Detection Algorithm based on Discrete Shearlet Transform Using Statistical Decision Theory

Baharak Ahmaderaghi, Fatih Kurugollu, Jesus Martinez del Rincon, Ahmed Bouridane

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Abstract

Blind watermarking targets the challenging recovery of the watermark when the host is not available during the detection stage.This paper proposes Discrete Shearlet Transform as a new embedding domain for blind image watermarking. Our novel DST blind watermark detection system uses a nonadditive scheme based on the statistical decision theory. It first computes the probability density function (PDF) of the DST coefficients modelled as a Laplacian distribution. The resulting likelihood ratio is compared with a decision threshold calculated using Neyman-Pearson criterion to minimise the missed detection subject to a fixed false alarm probability. Our method is evaluated in terms of imperceptibility, robustness and payload against different attacks (Gaussian noise, Blurring, Cropping, Compression and Rotation) using 30 standard grayscale images covering different characteristics (smooth, more complex with a lot of edges and high detail textured regions). The proposed method shows greater windowing flexibility with more sensitive to directional and anisotropic features when compared against Discrete Wavelet and Contourlets.
Original languageEnglish
Pages (from-to)46-59
Number of pages14
JournalIEEE Transactions on Computational Imaging
Volume4
Issue number1
Early online date15 Jan 2018
DOIs
Publication statusEarly online date - 15 Jan 2018

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