  Steganography is the art and science of hiding secret messages in public
communication so that the presence of the secret messages cannot be detected.
There are two distribution-preserving steganographic frameworks, one is
sampling-based and the other is compression-based. The former requires a
perfect sampler which yields data following the same distribution, and the
latter needs explicit distribution of generative objects. However, these two
conditions are too strict even unrealistic in the traditional data environment,
e.g. the distribution of natural images is hard to seize. Fortunately,
generative models bring new vitality to distribution-preserving steganography,
which can serve as the perfect sampler or provide the explicit distribution of
generative media. Take text-to-speech generation task as an example, we propose
distribution-preserving steganography based on WaveGlow and WaveNet, which
corresponds to the former two categories. Steganalysis experiments and
theoretical analysis are conducted to demonstrate that the proposed methods can
preserve the distribution.
