无需目标图像即可自动去除透明和不透明水印，保留语义内容。
Blind Visible Watermark Removal with Morphological Dilation
- 基于形态学膨胀的盲设水印移除方法，无需目标图像输入。
- 在多个数据集上比当前最佳方法提升50.8%的去水印效果。
- 适用于真实场景、隐写信息干扰等拓展应用，通用性强。
可见水印给图像修复技术带来重大挑战，尤其在目标背景未知时。为此，我们提出MorphoMod——一种在无目标图像前提下自动去除可见水印的新方法。与现有方法不同，MorphoMod能有效移除透明与不透明水印，同时保留语义内容，适合真实应用场景。在基准数据集（包括彩色大规模水印数据集CLWD、LOGO系列及新提出的Alpha1数据集）上的评估显示，MorphoMod相比最先进方法在去水印效果上最高提升50.8%。消融实验表明，提示词设计、预填充策略及修复模型性能对去水印效果有显著影响。此外，对隐写错位的案例研究揭示了水印移除在破坏高层隐藏信息方面的更广泛应用。MorphoMod提供了一种鲁棒且可扩展的水印移除方案，为图像修复与对抗性操作研究开辟新路径。
原文摘要 · Abstract (English)
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.
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