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

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, 4 (1). pp. 46-59. ISSN 2334-0118

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Ahmaderaghi et al - Blind Image Watermark Detection Algorithm Based on Discrete Shearlet Transform Using Statistical Decision Theory AAM.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.1109/TCI.2018.2794065

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 nonadditive scheme based on the statistical decision theory. It first computes the Probability Density Function (PDF) of the DST coefficients modeled as a Laplacian distribution. The resulting likelihood ratio is compared with a decision threshold calculated using Neyman-Pearson criterion to minimize 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.

Item Type: Article
Uncontrolled Keywords: Contourlet transform (CT), digital image watermarking, Discrete Wavelet Transform (DWT), Discrete Shearlet Transform (DST), frequency domain, laplacian distribution
Subjects: G100 Mathematics
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 13 Jun 2018 10:35
Last Modified: 14 Jun 2018 18:45
URI: http://nrl.northumbria.ac.uk/id/eprint/34517

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