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dc.contributor.authorAhmaderaghi, Baharak
dc.contributor.authorKurugollu, Fatih
dc.contributor.authorRincon, Jesus Martinez Del
dc.contributor.authorBouridane, Ahmed
dc.date.accessioned2019-03-20T11:49:46Z
dc.date.available2019-03-20T11:49:46Z
dc.date.issued2018-01-15
dc.identifier.citationAhmaderaghi, 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.en_US
dc.identifier.issn2333-9403
dc.identifier.issn2334-0118
dc.identifier.doi10.1109/TCI.2018.2794065
dc.identifier.urihttp://hdl.handle.net/10545/623626
dc.description.abstractBlind 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.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urlhttp://ieeexplore.ieee.org/document/8259288/en_US
dc.rightsArchived with thanks to IEEE Transactions on Computational Imagingen
dc.rights© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectDigital image watermarkingen_US
dc.subjectFrequency domainen_US
dc.subjectDSTen_US
dc.subjectDiscrete Wavelet Transformen_US
dc.subjectContourlet Transformen_US
dc.subjectLaplacian distributionen_US
dc.titleBlind image watermark detection algorithm based on discrete shearlet transform using statistical decision theoryen_US
dc.typeArticleen_US
dc.contributor.departmentQueen's University, Belfasten_US
dc.identifier.journalIEEE Transactions on Computational Imagingen_US
dcterms.dateAccepted2018-01-03
refterms.dateFOA2019-03-20T11:49:46Z
dc.author.detail785317en_US


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