Steganalysis of block-structured stegotext

We study a detection-theoretic approach to steganalysis. The relative entropy between covertext and stegotext determines the steganalyzer's difficulty in discriminating them, which in turn defines the detectability of the stegosystem. We consider the case of Gaussian random covertexts and mean-squared embedding constraint, We derive a lower bound on the relative entropy between covertext and stegotext for block-based embedding functions. This lower bound can be approached arbitrarily closely using a spread-spectrum method and secret keys with large entropy. The lower bound can also be attained using a stochastic quantization index modulation (QIM) encoder, without need for secret keys. In general, perfect undetectability can be achieved for blockwise memoryless Gaussian covertexts. For general Gaussian covertexts with memory, the relative entropy increases approximately linearly with the number of blocks observed by the steganalyzer. The error probabilities of the best steganalysis methods decrease exponentially with the number of blocks.