Optimal Watermark Embedding and Detection Strategies Under Limited Detection Resources

An information-theoretic approach is proposed to the watermark embedding and detection under limited detector resources. First, asymptotically optimal decision regions are presented in the Neyman-Pearson sense. These results are also modified to the case of zero-mean i.i.d. Gaussian covertext distribution with unknown variance. For this case, a lower bound is offered on the exponential decay rate of the false-negative probability and it is proven that the optimal embedding and detecting strategy is superior to the customary linear, additive embedding strategy in the exponential sense