In this paper

we propose a blind NSST domain image watermark decoder

wherein a vector-based HMT statistical model using BKF distribution is used. In the proposed scheme

the NSST is firstly performed on the original host image

and then the adaptive high-order watermark embedding strength functions are constructed

and finally the watermark data is embedded into the significant high-frequency coefficients in NSST domain. At the watermark receiver

NSST highpass coefficients are firstly modeled by employing the BKF vector HMT

where the BKF marginal statistics and strong intra-subband

cross-scale

and cross-orientation dependencies of NSST coefficients are incorporated. Then the statistical model parameters of BKF vector HMT are estimated using the expectation maximization approach. And finally a blind image watermark decoder is developed using BKF vector HMT and the maximum likelihood decision rule. The experimental results validate the effectiveness of the proposed technique.