Deep Residual Neural Networks for Image in Speech Steganography
This work addresses steganography for image-in-speech hiding, but it appears incremental as it builds on existing deep learning approaches without claiming broad breakthroughs.
The authors tackled the problem of hiding RGB images in speech segments without perceptual loss using deep learning, achieving minimal information loss in the reconstructed images.
Steganography is the art of hiding a secret message inside a publicly visible carrier message. Ideally, it is done without modifying the carrier, and with minimal loss of information in the secret message. Recently, various deep learning based approaches to steganography have been applied to different message types. We propose a deep learning based technique to hide a source RGB image message inside finite length speech segments without perceptual loss. To achieve this, we train three neural networks; an encoding network to hide the message in the carrier, a decoding network to reconstruct the message from the carrier and an additional image enhancer network to further improve the reconstructed message. We also discuss future improvements to the algorithm proposed.