Additive watermark detection in the wavelet domain using 2D-GARCH model

Watermark detection can be generally achieved by statistical decision methods. So, selection of an accurate statistical model is one of the major issues in watermark detection. Usually, watermark detectors assume the wavelet coefficients to be independent and identically distributed, therefore, they cannot take into account important characteristics of the wavelet coefficients such as dependency and heteroscedasticity. This paper presents a novel detector for wavelet domain additive image watermarking. We use two dimensional generalized autoregressive conditional heteroscedasticity (2D-GARCH) model for the wavelet coefficients. This model can capture the dependency, heteroscedasticity and heavy-tailed marginal distribution of these coefficients. Based on 2D-GARCH model, we design a new watermark detector and derive its receiver operating characteristics. Experimental results demonstrate the efficiency of proposed detector and its higher performance compared to alternative watermarking methods in the literature.