Color-image watermarking using multivariate power-exponential distribution

In this paper we present a novel watermark detector for additive spread-spectrum watermarking in the wavelet transform domain of color images. We propose to model the highly correlated DWT subbands of the RGB color channels by multivariate power-exponential distributions. This statistical model is then exploited to derive a likelihood ratio test for watermark detection. Our results indicate that joint statistical modeling of color DWT detail subbands leads to increased detection performance compared to previous approaches, namely watermarking of the luminance channel only, decorrelating the color bands, or relying on a joint Gaussian host signal model.

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