水印移除的法医成本：从专用攻击到图像编辑
The Forensic Cost of Watermark Removal: From Dedicated Attacks to Image Editing
AI总结：
本文提出水印移除检测（WRD）作为新评估维度，通过训练分类器检测移除痕迹，在10^{-3}假阳性率下实现最优检测，证明法医隐蔽性是水印移除的必要条件。
摘要：
当前水印移除方法在两个轴上进行评估：攻击成功率和感知质量。我们证明这是不够的。虽然最先进的攻击成功地在没有可见失真的情况下降低了水印信号，但它们留下了明显的统计伪影，暴露了移除尝试。我们将这个被忽视的轴命名为水印移除检测（WRD），并证明基于这些伪影训练的现代分类器在10^{-3}假阳性率下，对每种测试的移除方法都达到了最先进的检测率。没有现有的攻击考虑到这种法医泄漏。我们在扩展的评估三元组（攻击成功率、感知质量和法医可检测性）下，对领先的水印方案与标准移除流水线进行了基准测试，发现当前没有方法能平衡所有三个。我们的结果确立了法医隐蔽性作为水印移除的必要要求。
英文摘要：
Current watermark removal methods are evaluated on two axes: attack success rate and perceptual quality. We show this is insufficient. While state-of-the-art attacks successfully degrade the watermark signal without visible distortion, they leave distinct statistical artifacts that betray the removal attempt. We name this overlooked axis Watermark Removal Detection (WRD) and demonstrate that a modern classifier trained on these artifacts achieves state-of-the-art detection rates at $10^{-3}$ FPR across every removal method tested. No existing attack accounts for this forensic leakage. We benchmark leading watermarking schemes against standard removal pipelines under the extended evaluation triple of attack success, perceptual quality, and forensic detectability, and find that no current method balances all three. Our results establish forensic stealthiness as a necessary requirement for watermark removal.
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