A Brief Survey on Deep Learning Based Data Hiding, Steganography and Watermarking

Abstract

Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has provided enriching perspectives for it and made significant progress. In this work, we conduct a brief yet comprehensive review of existing literature and outline three meta-architectures. Based on this, we summarize specific strategies for various applications of deep hiding, including steganography, light field messaging and watermarking. Finally, further insight into deep hiding is provided through incorporating the perspective of adversarial attack.

Publication
In International Joint Conference on Artificial Intelligence (IJCAI 2021) Survey Track
Philipp Benz
Philipp Benz
Ph.D. Candidate @ Robotics and Computer Vision Lab, KAIST

My research interest is in Deep Learning with a focus on robustness and security.

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