  With the advent of the screen-reading era, the confidential documents
displayed on the screen can be easily captured by a camera without leaving any
traces. Thus, this paper proposes a novel screen-shooting resilient
watermarking scheme for document image using deep neural network. By applying
this scheme, when the watermarked image is displayed on the screen and captured
by a camera, the watermark can be still extracted from the captured
photographs. Specifically, our scheme is an end-to-end neural network with an
encoder to embed watermark and a decoder to extract watermark. During the
training process, a distortion layer between encoder and decoder is added to
simulate the distortions introduced by screen-shooting process in real scenes,
such as camera distortion, shooting distortion, light source distortion.
Besides, an embedding strength adjustment strategy is designed to improve the
visual quality of the watermarked image with little loss of extraction
accuracy. The experimental results show that the scheme has higher robustness
and visual quality than other three recent state-of-the-arts. Specially, even
if the shooting distances and angles are in extreme, our scheme can also obtain
high extraction accuracy.
