Abstract
Visible watermark removal which involves watermark cleaning and background content restoration is pivotal to evaluate the resilience of watermarks. Existing deep neural network (DNN)-based models still struggle with large-area watermarks and are overly dependent on the quality of watermark mask prediction. To overcome these challenges, we introduce a novel feature adapting framework that leverages the representation modeling capacity of a pre-trained image inpainting model. Our approach bridges the knowledge gap between image inpainting and watermark removal by fusing information of the residual background content beneath watermarks into the inpainting backbone model. We establish a dual-branch system to capture and embed features from the residual background content, which are merged into intermediate features of the inpainting backbone model via gated feature fusion modules. Moreover, for relieving the dependence on high-quality watermark masks, we introduce a new training paradigm by utilizing coarse watermark masks to guide the inference process. This contributes to a visible image removal model which is insensitive to the quality of watermark mask during testing. Extensive experiments on both a large-scale synthesized dataset and a real-world dataset demonstrate that our approach significantly outperforms existing state-of-the-art methods. The source code is available in the supplementary materials.
Abstract (translated)
可见水印去除技术，包括水印清洗和背景内容恢复，在评估水印的鲁棒性方面至关重要。现有的基于深度神经网络（DNN）的模型在处理大面积水印时仍然面临挑战，并且过度依赖于水印掩码预测的质量。为了解决这些问题，我们引入了一个新颖的功能适应框架，该框架利用了预训练图像修复模型的表示建模能力。我们的方法通过将残余背景内容的信息融入到图像修复主干模型中，填补了图像修复和水印去除之间的知识空白。我们建立了一个双分支系统来捕获并嵌入来自水印下的剩余背景内容的功能，并通过门控特征融合模块将其合并到图像修复主干模型的中间特性中。此外，为了减轻对高质量水印掩码的依赖性，我们引入了一种新的训练范式，利用粗略水印掩码来指导推理过程。这有助于创建一个在测试过程中对水印掩码质量不敏感的可见图片去除模型。广泛的实验结果表明，在大规模合成数据集和真实世界数据集上，我们的方法显著优于现有的最先进的方法。源代码可在补充材料中找到。
URL
https://arxiv.org/abs/2504.04687
