Data hiding is the process of embedding information into a noise-tolerant
signal such as a piece of audio, video, or image. Digital watermarking is a
form of data hiding where identifying data is robustly embedded so that it can
resist tampering and be used to identify the original owners of the media.
Steganography, another form of data hiding, embeds data for the purpose of
secure and secret communication. This survey summarises recent developments in
deep learning techniques for data hiding for the purposes of watermarking and
steganography, categorising them based on model architectures and noise
injection methods. The objective functions, evaluation metrics, and datasets
used for training these data hiding models are comprehensively summarised.
Finally, we propose and discuss possible future directions for research into
deep data hiding techniques