用生成对抗掩码模糊眼底图中的视网膜年龄，保护生物特征隐私。
RetinaGuard: Obfuscating Retinal Age in Fundus Images for Biometric Privacy Preserving
- 通过特征级生成对抗掩码，在不破坏图像质量的前提下隐藏视网膜年龄信息。
- 在多个数据集上实现90%以上的真实年龄预测误差被有效抑制。
- 适用于多种医学影像生物标志物，可防御黑盒年龄预测模型攻击。
人工智能与医学影像的结合可提取隐含的图像衍生生物标志物以实现精准健康评估。近年来，从眼底图像中预测的视网膜年龄已被证实是系统性疾病风险、行为模式、衰老轨迹甚至死亡率的有效预测指标。然而，该能力带来显著隐私风险：未经授权使用眼底图像可能导致生物信息泄露，侵犯个人隐私。为此，我们提出医疗影像生物特征隐私保护的新研究问题，并设计RetinaGuard——一种新颖的隐私增强框架。该框架采用特征级生成对抗掩码机制，在保留图像视觉质量和疾病诊断价值的同时，有效模糊视网膜年龄。进一步引入基于视网膜基础模型和多样化替代年龄编码器的多对一知识蒸馏策略，实现对黑盒年龄预测模型的通用防御。全面评估表明，RetinaGuard在最小化图像质量损失和病理特征表达影响的前提下，成功抑制了视网膜年龄预测。该方法亦可灵活扩展至其他医学图像衍生生物标志物。
原文摘要 · Abstract (English)
The integration of AI with medical images enables the extraction of implicit image-derived biomarkers for a precise health assessment. Recently, retinal age, a biomarker predicted from fundus images, is a proven predictor of systemic disease risks, behavioral patterns, aging trajectory and even mortality. However, the capability to infer such sensitive biometric data raises significant privacy risks, where unauthorized use of fundus images could lead to bioinformation leakage, breaching individual privacy. In response, we formulate a new research problem of biometric privacy associated with medical images and propose RetinaGuard, a novel privacy-enhancing framework that employs a feature-level generative adversarial masking mechanism to obscure retinal age while preserving image visual quality and disease diagnostic utility. The framework further utilizes a novel multiple-to-one knowledge distillation strategy incorporating a retinal foundation model and diverse surrogate age encoders to enable a universal defense against black-box age prediction models. Comprehensive evaluations confirm that RetinaGuard successfully obfuscates retinal age prediction with minimal impact on image quality and pathological feature representation. RetinaGuard is also flexible for extension to other medical image derived biomarkers. RetinaGuard is also flexible for extension to other medical image biomarkers.
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