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Data hiding is the art of concealing messages with limited perceptualchanges. Recently, deep learning has enriched it from various perspectives withsignificant progress. In this work, we conduct a brief yet comprehensive reviewof existing literature for deep learning based data hiding (deep hiding) byfirst classifying it according to three essential properties (i.e., capacity,security and robustness), and outline three commonly used architectures. Basedon this, we summarize specific strategies for different applications of datahiding, including basic hiding, steganography, watermarking and light fieldmessaging. Finally, further insight into deep hiding is provided byincorporating the perspective of adversarial attack.

A Brief Survey on Deep Learning Based Data Hiding