  Steganography is the art and science of hiding data within innocent-looking
objects (cover objects). Multimedia objects such as images and videos are an
attractive type of cover objects due to their high embedding rates. There exist
many techniques for performing steganography in both the literature and the
practical world. Meanwhile, the definition of the steganographic capacity for
multimedia and how to be calculated has not taken full attention. In this
paper, for multivariate quantized-Gaussian-distributed multimedia, we study the
maximum achievable embedding rate with respect to the statistical properties of
cover objects against the maximum achievable performance by any steganalytic
detector. Toward this goal, we evaluate the maximum allowed entropy of the
hidden message source subject to the maximum probability of error of the
steganalytic detector which is bounded by the KL-divergence between the
statistical distributions for the cover and the stego objects. We give the
exact scaling constant that governs the relationship between the entropies of
the hidden message and the cover object.
