In this work, a class of new blind watermark detectors is proposed for the DWT (Discrete Wavelet Transform)-based additive image watermarking problem. More specific, we model the marginal subband wavelet distributions with the Student-t probability density function (pdf) deriving a new watermark detector. The proposed detector shows high performance with regard to the watermark detection and increased robust properties against intentional or unintentional attacks. Experimental results on real images demonstrate these properties comparing the proposed detector with other state of the art methods in the transform domain.
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