A Brief Survey on Deep Learning Based Data Hiding
It provides a comprehensive review for researchers in data hiding, but is incremental as it synthesizes existing literature without new methods or results.
The paper surveys deep learning-based data hiding techniques, classifying them by capacity, security, and robustness, and outlining architectures and strategies for applications like steganography and watermarking.
Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has enriched it from various perspectives with significant progress. In this work, we conduct a brief yet comprehensive review of existing literature for deep learning based data hiding (deep hiding) by first classifying it according to three essential properties (i.e., capacity, security and robustness), and outline three commonly used architectures. Based on this, we summarize specific strategies for different applications of data hiding, including basic hiding, steganography, watermarking and light field messaging. Finally, further insight into deep hiding is provided by incorporating the perspective of adversarial attack.