  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.
