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

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    IEEE Tr Computational Imaging ...
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    Authors
    Ahmaderaghi, Baharak cc
    Kurugollu, Fatih cc
    Rincon, Jesus Martinez Del cc
    Bouridane, Ahmed cc
    Affiliation
    Queen's University, Belfast
    Issue Date
    2018-01-15
    
    Metadata
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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 (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.
    Citation
    Ahmaderaghi, B., Kurugollu, F., Rincon, J., and Bouridane, A. (2018) ‘Blind image watermark detection algorithm based on discrete shearlet transform using statistical decision theory’. IEEE Transactions on Computational Imaging (4)1, pp. 46-59. DOI: 10.1109/TCI.2018.2794065.
    Publisher
    IEEE
    Journal
    IEEE Transactions on Computational Imaging
    URI
    http://hdl.handle.net/10545/623626
    DOI
    10.1109/TCI.2018.2794065
    Additional Links
    http://ieeexplore.ieee.org/document/8259288/
    Type
    Article
    Language
    en
    ISSN
    2333-9403
    2334-0118
    ae974a485f413a2113503eed53cd6c53
    10.1109/TCI.2018.2794065
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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