  Digital watermark is a commonly used technique to protect the copyright of
medias. Simultaneously, to increase the robustness of watermark, attacking
technique, such as watermark removal, also gets the attention from the
community. Previous watermark removal methods require to gain the watermark
location from users or train a multi-task network to recover the background
indiscriminately. However, when jointly learning, the network performs better
on watermark detection than recovering the texture. Inspired by this
observation and to erase the visible watermarks blindly, we propose a novel
two-stage framework with a stacked attention-guided ResUNets to simulate the
process of detection, removal and refinement. In the first stage, we design a
multi-task network called SplitNet. It learns the basis features for three
sub-tasks altogether while the task-specific features separately use multiple
channel attentions. Then, with the predicted mask and coarser restored image,
we design RefineNet to smooth the watermarked region with a mask-guided spatial
attention. Besides network structure, the proposed algorithm also combines
multiple perceptual losses for better quality both visually and numerically. We
extensively evaluate our algorithm over four different datasets under various
settings and the experiments show that our approach outperforms other
state-of-the-art methods by a large margin. The code is available at
http://github.com/vinthony/deep-blind-watermark-removal.
