Optimal Watermark Embedding and Detection Strategies Under Limited Detection Resources

An information-theoretic approach is proposed to watermark embedding and detection under limited detector resources. First, the attack-free scenario is considered under which asymptotically optimal decision regions in the Neyman-Pearson sense are proposed, along with the optimal embedding rule. Later, the case of zero-mean independent and identically distributed (i.i.d.) Gaussian covertext distribution is explored with unknown variance under the attack-free scenario. For this case, a lower bound on the exponential decay rate of the false-negative probability is proposed. It is proven that the optimal embedding and detecting strategy is superior to the customary linear, additive embedding strategy in the exponential sense. Finally, these results are extended to the case of memoryless attacks and general worst case attacks. Optimal decision regions and embedding rules are offered, and the worst attack channel is identified.