A Robust Multibit Multiplicative Watermark Decoder Using a Vector-Based Hidden Markov Model in Wavelet Domain

The vector-based hidden Markov model (HMM) is a powerful statistical model for characterizing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross-orientation dependencies of the wavelet coefficients. In...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2018-02, Vol.28 (2), p.402-413
Main Authors: Amini, Marzieh, Ahmad, M. Omair, Swamy, M.N.S.
Format: Article
Language:English
Subjects:
Bit error rate
Computer simulation
Decoders
Decoding
Detectors
Hidden Markov model (HMM)
Hidden Markov models
image watermarking
Markov analysis
Markov chains
Maximum likelihood decoding
Monte Carlo simulation
optimum watermark decoder
Watermarking
Wavelet analysis
Wavelet domain
wavelet transform
Wavelet transforms
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Summary:The vector-based hidden Markov model (HMM) is a powerful statistical model for characterizing the distribution of the wavelet coefficients, since it is capable of capturing the subband marginal distribution as well as the inter-scale and cross-orientation dependencies of the wavelet coefficients. In this paper we propose a scheme for designing a blind multibit watermark decoder incorporating the vector-based HMM in wavelet domain. The decoder is designed based on the maximum likelihood criterion. A closed-form expression is derived for the bit error rate and validated experimentally with Monte Carlo simulations. The performance of the proposed watermark detector is evaluated using a set of standard test images and shown to outperform the decoders designed based on the Cauchy or generalized Gaussian distributions without or with attacks. It is also shown that the proposed decoder is more robust against various kinds of attacks compared with the state-of-the-art methods.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2016.2607299