无透镜注视并非默认隐私：跨披露面的身份泄露审计
原标题：Lensless Gaze Is Not Private by Default: Auditing Identity Leakage Across Disclosure Surfaces
AI 摘要
该研究审计了无透镜注视追踪管线的身份隐私泄露问题，发现尽管编码测量在视觉上不可理解，但学习型攻击者仍可从多个披露面恢复身份信息。在模拟的OpenEDS数据集上，原始裁剪与无透镜测量的top-1识别准确率接近，MAE嵌入、PCA投影和GSPL瓶颈均保留显著的身份可恢复性。研究强调隐私声明必须在披露边界上测试，而非从视觉外观推断，并发布了代码。
正文节选
Lensless Gaze Is Not Private by Default: Auditing Identity Leakage Across Disclosure Surfaces Abstract Lensless near-eye sensing is often described as privacy-friendly because its coded measurements are visually unintelligible. Yet visual unintelligibility reflects human interpretation, not what a learned adversary can recover. We therefore treat identity privacy as a systems property of disclosure surfaces: representations crossing sensing, storage, computation, and output boundaries. We audit