AI 中文总结
研究针对医学图像外包建模的隐私问题，建立统一框架评估DisguisedNets和NeuraCrypt等方法，分析其在多数据集上的预测效用、效率及抗攻击鲁棒性，发现图像伪装性能因任务而异，RMT平衡最佳，为医学AI应用中PET适用性提供评估。
AI 中文摘要
基于云的深度学习可实现大规模医学图像分析，但将敏感患者图像外包用于模型开发时会引发重大隐私问题。图像伪装作为一种有前途的隐私增强技术（PET）应运而生，它能将图像转换为视觉上难以理解的表示形式，同时保留用于下游学习的信息。我们建立了一个统一框架，在涉及分类和语义分割任务的四个数据集上评估代表性方法DisguisedNets和NeuraCrypt。我们的分析评估了预测效用、效率以及对重建攻击的鲁棒性。结果表明，图像伪装性能在不同任务之间差异显著；虽然这些方法保留了医学图像分类的效用，但在密集语义分割中导致了大幅退化。具体而言，随机多维变换（RMT）提供了性能和安全性的最佳平衡，而基于AES的伪装严重影响了效用。此外，在自然图像上有效的基于回归的重建攻击在实际医学图像上的成功率要低得多。这些发现为PET在机密医学人工智能应用中的适用性提供了系统评估。
英文摘要
Cloud-based deep learning enables large-scale medical image analysis but raises significant privacy concerns when sensitive patient images are outsourced for model development. Image disguising has recently emerged as a promising privacy-enhancing technology (PET) that transforms images into visually unintelligible representations while preserving information for downstream learning. We established a unified framework to evaluate representative methods, DisguisedNets and NeuraCrypt, across four datasets involving classification and semantic segmentation tasks. Our analysis assessed predictive utility, efficiency, and robustness against reconstruction attacks. Results showed that image disguising performance varies significantly between tasks; while methods preserved utility for medical image classification, they caused substantial degradation in dense semantic segmentation. Specifically, Randomized Multidimensional Transformation (RMT) offered the optimal balance of performance and security, whereas AES-based disguising severely impacted utility. Furthermore, regression-based reconstruction attacks effective on natural images proved considerably less successful on realistic medical images. These findings provide a systematic assessment of PET suitability for confidential medical AI applications.
