Distribution-Preserving Steganography Based on Text-to-Speech Generative Models
Kejiang Chen, Hang Zhou, Hanqing Zhao, Dongdong Chen, Weiming Zhang, Nenghai Yu
Abstract
Steganography is the art and science of hiding secret messages in public communication so that the presence of secret messages cannot be detected. There are two distribution-preserving steganographic frameworks, one is sampler-based and the other is compression-based. The former requires a perfect sampler which yields data following the same distribution, and the latter needs the 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. Taking text-to-speech generation task as an example, we propose distribution-preserving steganography based on WaveGlow and WaveRNN, which corresponds to the former two categories. Steganalysis experiments and theoretical analysis are conducted to demonstrate that the proposed methods can preserve the distribution.