Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
It addresses the problem of secure data embedding for researchers and practitioners in cybersecurity and digital rights management, but is incremental as it synthesizes existing work without introducing new methods.
This survey reviews deep learning techniques for data hiding, covering digital watermarking and steganography to protect intellectual property and enable secure communication, with a focus on model architectures, noise injection methods, and evaluation metrics.
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.