A Brief Survey on Deep Learning Based Data Hiding
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by
Chaoning Zhang, Chenguo Lin, Philipp Benz, Kejiang Chen, Weiming Zhang, In So Kweon
2022
Abstract
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.
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