  An information-theoretic approach is proposed to watermark embedding and
detection under limited detector resources. First, we consider the attack-free
scenario under which asymptotically optimal decision regions in the
Neyman-Pearson sense are proposed, along with the optimal embedding rule.
Later, we explore the case of zero-mean i.i.d. Gaussian covertext distribution
with unknown variance under the attack-free scenario. For this case, we propose
a lower bound on the exponential decay rate of the false-negative probability
and prove 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.
