Blind image watermark detection algorithm based on discrete shearlet transform using statistical decision theory

Journal article


Ahmaderaghi, Baharak, Kurugollu, Fatih, Rincon, Jesus Martinez Del and Bouridane, Ahmed 2018. Blind image watermark detection algorithm based on discrete shearlet transform using statistical decision theory. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/TCI.2018.2794065
AuthorsAhmaderaghi, Baharak, Kurugollu, Fatih, Rincon, Jesus Martinez Del and Bouridane, Ahmed
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 (DST) as a new embedding domain for blind image watermarking. Our novel DST blind watermark detection system uses a non-additive 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.

KeywordsDigital image watermarking; Frequency domain; DST; Discrete Wavelet Transform; Contourlet Transform; Laplacian distribution
Year2018
JournalIEEE Transactions on Computational Imaging
PublisherIEEE
ISSN2333-9403
2334-0118
Digital Object Identifier (DOI)https://doi.org/10.1109/TCI.2018.2794065
Web address (URL)http://hdl.handle.net/10545/623626
hdl:10545/623626
Publication dates15 Jan 2018
Publication process dates
Deposited20 Mar 2019, 11:49
Accepted03 Jan 2018
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ContributorsQueen's University, Belfast
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