MarkCleaner: 通过不可察觉的微几何扰动实现高保真的水印移除
MarkCleaner: High-Fidelity Watermark Removal via Imperceptible Micro-Geometric Perturbation
AI总结：
MarkCleaner通过微几何扰动实现高效且高保真的水印移除，避免语义漂移并提升视觉保真度。
摘要：
语义水印对传统图像空间攻击具有很强的鲁棒性。在本工作中，我们表明这种鲁棒性在微几何扰动下并不成立：空间位移可以通过破坏相位对齐来移除水印。受此观察启发，我们引入了MarkCleaner，一种避免因再生类水印移除导致语义漂移的水印移除框架。具体而言，MarkCleaner通过微几何扰动的监督进行训练，促使模型将语义内容与严格的空间对齐分开，并在细微的几何位移下实现稳健的重建。该框架采用引导掩码的编码器，学习显式空间表示，并采用基于2D高斯点散列的解码器，显式参数化几何扰动同时保持语义内容。大量实验表明，MarkCleaner在水印移除效果和视觉保真度方面均取得优异表现，同时支持高效的实时推理。我们的代码将在接受后提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
