A Brief Survey on Deep Learning Based Data Hiding
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.
Cite
@article{arxiv.2103.01607,
title = {A Brief Survey on Deep Learning Based Data Hiding},
author = {Chaoning Zhang and Chenguo Lin and Philipp Benz and Kejiang Chen and Weiming Zhang and In So Kweon},
journal= {arXiv preprint arXiv:2103.01607},
year = {2022}
}
Comments
v2: reorganize some sections and add several new papers published in 2021~2022