Semantic watermarks exhibit strong robustness against conventional image-space attacks. In this work, we show that such robustness does not survive under micro-geometric perturbations: spatial displacements can remove watermarks by breaking the phase alignment. Motivated by this observation, we introduce MarkCleaner, a watermark removal framework that avoids semantic drift caused by regeneration-based watermark removal. Specifically, MarkCleaner is trained with micro-geometry-perturbed supervision, which encourages the model to separate semantic content from strict spatial alignment and enables robust reconstruction under subtle geometric displacements. The framework adopts a mask-guided encoder that learns explicit spatial representations and a 2D Gaussian Splatting-based decoder that explicitly parameterizes geometric perturbations while preserving semantic content. Extensive experiments demonstrate that MarkCleaner achieves superior performance in both watermark removal effectiveness and visual fidelity, while enabling efficient real-time inference. Our code will be made available upon acceptance.

翻译：语义水印对传统图像空间攻击展现出极强的鲁棒性。本研究表明，此类鲁棒性在微观几何扰动下难以维持：空间位移可通过破坏相位对齐实现水印去除。基于此发现，我们提出MarkCleaner水印去除框架，该框架避免了基于再生成的水印去除方法引发的语义漂移问题。具体而言，MarkCleaner采用微观几何扰动监督进行训练，促使模型将语义内容与严格空间对齐解耦，从而在细微几何位移下实现鲁棒重建。该框架采用掩码引导编码器学习显式空间表征，并基于2D高斯泼溅的解码器显式参数化几何扰动同时保持语义内容。大量实验表明，MarkCleaner在水印去除效果与视觉保真度方面均取得优越性能，同时支持高效的实时推理。代码将在论文录用后公开。