  Watermarking is an important copyright protection technology which generally
embeds the identity information into the carrier imperceptibly. Then the
identity can be extracted to prove the copyright from the watermarked carrier
even after suffering various attacks. Most of the existing watermarking
technologies take the nature images as carriers. Different from the natural
images, document images are not so rich in color and texture, and thus have
less redundant information to carry watermarks. This paper proposes an
end-to-end document image watermarking scheme using the deep neural network.
Specifically, an encoder and a decoder are designed to embed and extract the
watermark. A noise layer is added to simulate the various attacks that could be
encountered in reality, such as the Cropout, Dropout, Gaussian blur, Gaussian
noise, Resize, and JPEG Compression. A text-sensitive loss function is designed
to limit the embedding modification on characters. An embedding strength
adjustment strategy is proposed to improve the quality of watermarked image
with little loss of extraction accuracy. Experimental results show that the
proposed document image watermarking technology outperforms three
state-of-the-arts in terms of the robustness and image quality.
