  The advancement of secure communication and identity verification fields has
significantly increased through the use of deep learning techniques for data
hiding. By embedding information into a noise-tolerant signal such as audio,
video, or images, digital watermarking and steganography techniques can be used
to protect sensitive intellectual property and enable confidential
communication, ensuring that the information embedded is only accessible to
authorized parties. This survey provides an overview of recent developments in
deep learning techniques deployed for data hiding, categorized systematically
according to model architectures and noise injection methods. The objective
functions, evaluation metrics, and datasets used for training these data hiding
models are comprehensively summarised. Additionally, potential future research
directions that unite digital watermarking and steganography on software
engineering to enhance security and mitigate risks are suggested and
deliberated. This contribution furthers the creation of a more trustworthy
digital world and advances Responsible AI.
