Abstract
Visible watermarks pose significant challenges for image restoration techniques, especially when the target background is unknown. Toward this end, we present MorphoMod, a novel method for automated visible watermark removal that operates in a blind setting -- without requiring target images. Unlike existing methods, MorphoMod effectively removes opaque and transparent watermarks while preserving semantic content, making it well-suited for real-world applications. Evaluations on benchmark datasets, including the Colored Large-scale Watermark Dataset (CLWD), LOGO-series, and the newly introduced Alpha1 datasets, demonstrate that MorphoMod achieves up to a 50.8% improvement in watermark removal effectiveness compared to state-of-the-art methods. Ablation studies highlight the impact of prompts used for inpainting, pre-removal filling strategies, and inpainting model performance on watermark removal. Additionally, a case study on steganographic disorientation reveals broader applications for watermark removal in disrupting high-level hidden messages. MorphoMod offers a robust, adaptable solution for watermark removal and opens avenues for further advancements in image restoration and adversarial manipulation.
Abstract (translated)
可见水印对图像恢复技术构成了重大挑战，尤其是在目标背景未知的情况下。为此，我们提出了MorphoMod，这是一种新颖的自动化去除可见水印的方法，在盲处理环境下工作——无需提供目标图片。与现有方法不同的是，MorphoMod能够有效移除不透明和半透明的水印，并且在保留语义内容的同时进行操作，使其非常适合现实世界的应用。 在包括Colored Large-scale Watermark Dataset (CLWD)，LOGO系列以及新引入的Alpha1数据集在内的基准数据集上进行评估显示，MorphoMod相比最先进的方法，在水印去除效果方面提高了高达50.8%。消融研究表明了用于修复过程中的提示、预移除填充策略和修复模型性能对水印去除的影响。此外，一项关于隐写术定向的研究案例揭示了水印去除在干扰高级隐藏信息方面的更广泛应用。 MorphoMod为水印去除提供了一种稳健且适应性强的解决方案，并开启了图像恢复及对抗性操作领域进一步发展的新途径。
URL
https://arxiv.org/abs/2502.02676
