论文标题
WDNET：可见的水印去除网络分类网络
WDNet: Watermark-Decomposition Network for Visible Watermark Removal
论文作者
论文摘要
可见的水印在图像中广泛使用，以保护版权所有权。分析去除水印有助于以对抗性方式加强反攻击技术。当前的去除方法通常利用图像到图像翻译技术。然而，水印的大小，形状，颜色和透明度的不确定性为这些方法设定了巨大的障碍。为了对抗这一点，我们将传统的水印图像分解结合到一个称为水印的分解网络（WDNET）的两个阶段发电机中，在该机构中，第一阶段预测了整个水印图像的粗糙分解，第二阶段专门集中在水印区域上，以改进去除结果。分解配方使WDNET能够将水印与图像分开，而不是简单地将其除去。我们进一步表明，这些分离的水印可以用作构建更大训练数据集并进一步改善删除性能的额外营养。此外，我们构建了一个名为CLWD的大型数据集（主要包含彩色水印），以填充彩色水印去除数据集的真空。公共灰度数据集LVW和CLWD的广泛实验始终表明，所提出的WDNET在准确性和效率方面都优于最先进的方法。代码和CLWD数据集可在https://github.com/mruil/wdnet上公开获取。
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 https://github.com/MRUIL/WDNet.
