RetinaGuard：用于生物特征隐私保护的眼底图像视网膜年龄混淆
RetinaGuard: Obfuscating Retinal Age in Fundus Images for Biometric Privacy Preserving
AI总结：
针对眼底图像中视网膜年龄推断导致的生物特征隐私泄露问题，提出RetinaGuard框架，利用特征级生成对抗掩蔽与多对一知识蒸馏，在保留图像质量和诊断效用的同时，有效混淆年龄预测，并可扩展至其他医学图像生物标志物。
摘要：
人工智能与医学图像的整合使得能够提取隐含的图像衍生生物标志物，用于精确的健康评估。近年来，视网膜年龄作为一种从眼底图像预测的生物标志物，已被证明是全身性疾病风险、行为模式、衰老轨迹甚至死亡率的预测因子。然而，推断此类敏感生物特征数据的能力带来了显著的隐私风险，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，我们提出了一个与医学图像相关的生物特征隐私新研究问题，并提出了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蔽机制来混淆视网膜年龄，同时保留图像视觉质量和疾病诊断效用。该框架进一步利用了一种新颖的多对一知识蒸馏策略，结合视网膜基础模型和多种替代年龄编码器，以实现对黑盒年龄预测模型的通用防御。综合评估证实，RetinaGuard成功混淆了视网膜年龄预测，对图像质量和病理特征表示的影响极小。RetinaGuard还可灵活扩展到其他医学图像衍生的生物标志物。RetinaGuard还可灵活扩展到其他医学图像生物标志物。
英文摘要：
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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