论文标题
拆分然后改进：堆叠的注意引导的重新设备，用于盲目的单图像可见水印去除
Split then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal
论文作者
论文摘要
数字水印是一种保护媒体版权的常用技术。同时，为了提高水印的鲁棒性，攻击技术（例如去除水印）也引起了社区的关注。先前的水印方法需要从用户获得水印位置或训练多任务网络以不可差的方式恢复背景。但是，当共同学习时，网络在水印检测方面的表现要比恢复纹理更好。受这一观察结果的启发，并盲目地擦除了可见的水印，我们提出了一个新型的两阶段框架，并带有堆叠的注意引导重新设备，以模拟检测，去除和改进的过程。在第一阶段，我们设计了一个名为SplitNet的多任务网络。它完全学习了三个子任务的基础功能，而特定于任务的功能则分别使用多个通道专注。然后，使用预测的掩模和更粗的恢复图像，我们设计了粉刷以通过掩盖引导的空间注意力使水印区域平滑。除了网络结构外，所提出的算法还结合了多个感知损失，以视觉和数值上的质量提高。我们在各种设置下在四个不同的数据集上广泛评估了我们的算法，并且实验表明，我们的方法的表现优于其他最先进的方法。该代码可在http://github.com/vinthony/deep-blind-watermark-removal上找到。
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.
