X-Mark: 基于显著性的鲁棒数据集所有权验证用于医学影像
X-Mark: Saliency-Guided Robust Dataset Ownership Verification for Medical Imaging
AI总结：
X-Mark通过条件U-Net在医学影像显著区域生成独特扰动，实现鲁棒的数据集所有权验证，有效防止版权侵权并降低误报率。
摘要：
高质量的医学影像数据集对于训练深度学习模型至关重要，但其未经授权的使用引发了严重的版权和伦理问题。医学影像对现有针对自然图像设计的数据集所有权验证方法构成了独特挑战，因为静态水印图案在固定尺度图像中生成，在动态和高分辨率扫描中缩放效果差，而这些扫描具有有限的视觉多样性及细微的解剖结构，同时保持诊断质量。在本文中，我们提出了X-Mark，一种针对胸片版权保护的样本特定清洁标签水印方法。具体而言，X-Mark使用条件U-Net在每个样本的显著区域内生成独特的扰动。我们设计了一个多组件训练目标，以确保水印的有效性，同时在保持诊断质量及视觉可区分性的同时，对动态缩放过程具有鲁棒性。我们还将拉普拉斯正则化纳入训练目标，以惩罚高频扰动并实现水印缩放不变性。所有权验证在黑盒设置中进行，以检测可疑模型中的特征行为。在CheXpert上的大量实验验证了X-Mark的有效性，实现了100%的WSR，并在Ind-M场景中将误报概率降低了12%，同时展示了对潜在自适应攻击的抵抗力。
英文摘要：
High-quality medical imaging datasets are essential for training deep learning models, but their unauthorized use raises serious copyright and ethical concerns. Medical imaging presents a unique challenge for existing dataset ownership verification methods designed for natural images, as static watermark patterns generated in fixed-scale images scale poorly dynamic and high-resolution scans with limited visual diversity and subtle anatomical structures, while preserving diagnostic quality. In this paper, we propose X-Mark, a sample-specific clean-label watermarking method for chest x-ray copyright protection. Specifically, X-Mark uses a conditional U-Net to generate unique perturbations within salient regions of each sample. We design a multi-component training objective to ensure watermark efficacy, robustness against dynamic scaling processes while preserving diagnostic quality and visual-distinguishability. We incorporate Laplacian regularization into our training objective to penalize high-frequency perturbations and achieve watermark scale-invariance. Ownership verification is performed in a black-box setting to detect characteristic behaviors in suspicious models. Extensive experiments on CheXpert verify the effectiveness of X-Mark, achieving WSR of 100% and reducing probability of false positives in Ind-M scenario by 12%, while demonstrating resistance to potential adaptive attacks.
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。
