Blind image watermark detection algorithm based on discrete shearlet transform using statistical decision theory
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
IEEEJournal
IEEE Transactions on Computational ImagingDOI
10.1109/TCI.2018.2794065Additional Links
http://ieeexplore.ieee.org/document/8259288/Type
ArticleLanguage
enISSN
2333-94032334-0118
ae974a485f413a2113503eed53cd6c53
10.1109/TCI.2018.2794065