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
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 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.
Original language | English |
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Pages (from-to) | 46-59 |
Number of pages | 14 |
Journal | IEEE Transactions on Computational Imaging |
Volume | 4 |
Issue number | 1 |
Early online date | 15 Jan 2018 |
DOIs | |
Publication status | Early online date - 15 Jan 2018 |