Information-Theoretic Limits for Steganography in Multimedia
This work addresses the theoretical limits of steganographic capacity in multimedia, which is important for security and privacy applications, but it is incremental as it builds on existing information-theoretic frameworks.
The paper tackles the problem of determining the maximum achievable embedding rate for steganography in multimedia, specifically for multivariate quantized-Gaussian-distributed objects, by evaluating the entropy of hidden messages relative to steganalytic detector performance, and provides an exact scaling constant for this relationship.
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.