# X-Mark: Saliency-Guided Robust Dataset Ownership Verification for Medical Imaging

Source: [X-Mark: Saliency-Guided Robust Dataset Ownership Verification for Medical Imaging](<https://www.sota2.com/research/paper/698c0d00c97a0b3e3b5e1119>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-12T05:00:04.497Z

Authors: Pranav Kulkarni, Junfeng Guo, Heng Huang

Published: 2026-02-10T00:03:43.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Dataset Watermarking on CheXpert (test)](<https://www.sota2.com/research/sota/dataset-watermarking-on-chexpert-test.md>): Dataset Watermarking; CheXpert (test); BA: 84.88.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2602.09284>)

[PDF](<https://arxiv.org/pdf/2602.09284.pdf>)