Cross-domain redundancy exploration by a deep encoder–decoder network for speech steganography

Abstract

The technique of speech steganography involves embedding messages within openly transmitted speech channels without arousing suspicion. Nevertheless, current methods for embedding speech in speech suffer from weak imperceptibility and low message speech intelligibility. In this paper, we introduce a novel approach that explores cross-domain redundancy by leveraging a deep encoder–decoder neural network architecture to embed Mel-spectrograms into magnitude spectrograms. Specifically, the message is transformed into its Mel-spectrogram, while the cover is transformed into its magnitude spectrogram. Subsequently, the Mel-spectrogram is embedded as residuals in the magnitude spectrogram through an encoder known as the spectrogram super-resolution network (SSRN). Upon receiving the stego, a decoder network recoveres the Mel-spectrograms of the messages, and a high-fidelity HiFi-GAN vocoder then recovers the message waveform. The encoder–decoder network’s parameters are optimized to ensure imperceptibility and high quality. To validate the superiority of our proposed method, we compare it with recently proposed baselines using common databases such as the LJ Speech and VCTK datasets. Experimental results demonstrate that our method achieves SNRs of 33.83 dB and 30.28 dB for the cover signals on these two datasets, respectively. Furthermore, both the content and speaker identity of the recovered messages are well preserved, and the experiments also confirm the robustness against noises and the security of our approach.