| Title: | StealthMark: Harmless and Stealthy Ownership Verification for Medical Segmentation via Uncertainty-Guided Backdoors |
| Journal: | IEEE Transactions on Image Processing |
| Published: | 28 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41605147/ |
| DOI: | https://doi.org/10.1109/tip.2026.3655563 |
| URL: | https://orca.cardiff.ac.uk/id/eprint/184818/1/Qinkai_TIP_watermark.pdf |
Publication 18446
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Abstract
Annotating medical data for training AI models is often costly and limited due to the shortage of specialists with relevant clinical expertise. This challenge is further compounded by privacy and ethical concerns associated with sensitive patient information. As a result, well-trained medical segmentation models on private datasets constitute valuable intellectual property requiring robust protection mechanisms. Existing model protection techniques primarily focus on classification and generative tasks, while segmentation models-crucial to medical image analysis-remain largely underexplored. In this paper, we propose a novel, stealthy, and harmless method, StealthMark, for verifying the ownership of medical segmentation models under closed-box conditions. Our approach subtly modulates model uncertainty without altering the final segmentation outputs, thereby preserving the model's performance. To enable ownership verification, we incorporate model-agnostic explanation methods, e.g. LIME, to extract feature attributions from the model outputs. Under specific triggering conditions, these explanations reveal a distinct and verifiable watermark. We further design the watermark as a QR code to facilitate robust and recognizable ownership claims. We conducted extensive experiments across four medical imaging datasets (CMR dataset from UK Biobank, the SEG fundus dataset, the EchoNet echocardiography dataset, and the PraNet colonoscopy dataset) and five mainstream segmentation models. The results demonstrate the effectiveness, stealthiness, and harmlessness of our method on the original model's segmentation performance. For example, when applied to the SAM model, StealthMark consistently achieved attack success rates (ASR) above 95% across various datasets while maintaining less than a 1% drop in Dice and AUC scores-significantly outperforming backdoor-based watermarking methods and highlighting its strong potential for practical deployment. Our implementation code is made available at https://github.com/Qinkaiyu/StealthMark.</p>
8 Keywords
- Algorithms
- Artificial Intelligence
- Databases, Factual
- Diagnostic Imaging
- Humans
- Image Processing, Computer-Assisted
- Ownership
- Uncertainty
14 Authors
- Qinkai Yu
- Chong Zhang
- Gaojie Jin
- Tianjin Huang
- Wei Zhou
- Wenhui Li
- Xiaobo Jin
- Bo Huang
- Yitian Zhao
- Guang Yang
- Gregory Y. H. Lip
- Yalin Zheng
- Aline Villavicencio
- Yanda Meng
1 Application
| Application ID | Title |
|---|---|
| 54078 | AI Enabled Structure and Function Analysis for Establishing the Eye as the Window to the Heart and Brain |
Enabling scientific discoveries that improve human health