Visible watermarks are widely-used in images to protect copyright ownership.
Analyzing watermark removal helps to reinforce the anti-attack techniques in an
adversarial way. Current removal methods normally leverage image-to-image
translation techniques. Nevertheless, the uncertainty of the size, shape, color
and transparency of the watermarks set a huge barrier for these methods. To
combat this, we combine traditional watermarked image decomposition into a
two-stage generator, called Watermark-Decomposition Network (WDNet), where the
first stage predicts a rough decomposition from the whole watermarked image and
the second stage specifically centers on the watermarked area to refine the
removal results. The decomposition formulation enables WDNet to separate
watermarks from the images rather than simply removing them. We further show
that these separated watermarks can serve as extra nutrients for building a
larger training dataset and further improving removal performance. Besides, we
construct a large-scale dataset named CLWD, which mainly contains colored
watermarks, to fill the vacuum of colored watermark removal dataset. Extensive
experiments on the public gray-scale dataset LVW and CLWD consistently show
that the proposed WDNet outperforms the state-of-the-art approaches both in
accuracy and efficiency. The code and CLWD dataset are publicly available at
this https URL

该研究提出了一种名为 Watermark-Decomposition Network（WDNet）的二阶段生成器，旨在分离图片中的水印，而不仅仅是简单地删除它们，并通过构建一个命名为 CLWD 的大规模数据集来显示 WDNet 在可见水印识别和去除性能上的优越性