EgoPrivacy：你的第一人称摄像头揭示了什么？

EgoPrivacy: What Your First-Person Camera Says About You?

June 13, 2025

作者: Yijiang Li, Genpei Zhang, Jiacheng Cheng, Yi Li, Xiaojun Shan, Dashan Gao, Jiancheng Lyu, Yuan Li, Ning Bi, Nuno Vasconcelos

cs.AI

摘要

随着可穿戴摄像头的迅速普及，关于第一人称视角视频隐私的重大关切日益凸显，然而先前的研究大多忽视了摄像头佩戴者所面临的独特隐私威胁。本研究探讨了一个核心问题：从佩戴者的第一人称视角视频中，能够推断出多少关于其隐私的信息？我们引入了EgoPrivacy，这是首个用于全面评估第一人称视觉隐私风险的大规模基准。EgoPrivacy涵盖了三种隐私类型（人口统计、个人及情境），定义了七项任务，旨在从细粒度（如佩戴者身份）到粗粒度（如年龄组）恢复私人信息。为了进一步强调第一人称视觉固有的隐私威胁，我们提出了检索增强攻击，这是一种新颖的攻击策略，它通过从外部库中的第三人称视频进行第一人称到第三人称的检索，来增强人口统计隐私攻击的效果。我们对所有威胁模型下可能的不同攻击进行了广泛比较，结果表明佩戴者的隐私信息极易泄露。例如，我们的研究发现，基础模型即使在零样本设置下也能有效侵害佩戴者隐私，通过恢复身份、场景、性别和种族等属性，准确率高达70-80%。我们的代码和数据可在https://github.com/williamium3000/ego-privacy获取。

English

While the rapid proliferation of wearable cameras has raised significant concerns about egocentric video privacy, prior work has largely overlooked the unique privacy threats posed to the camera wearer. This work investigates the core question: How much privacy information about the camera wearer can be inferred from their first-person view videos? We introduce EgoPrivacy, the first large-scale benchmark for the comprehensive evaluation of privacy risks in egocentric vision. EgoPrivacy covers three types of privacy (demographic, individual, and situational), defining seven tasks that aim to recover private information ranging from fine-grained (e.g., wearer's identity) to coarse-grained (e.g., age group). To further emphasize the privacy threats inherent to egocentric vision, we propose Retrieval-Augmented Attack, a novel attack strategy that leverages ego-to-exo retrieval from an external pool of exocentric videos to boost the effectiveness of demographic privacy attacks. An extensive comparison of the different attacks possible under all threat models is presented, showing that private information of the wearer is highly susceptible to leakage. For instance, our findings indicate that foundation models can effectively compromise wearer privacy even in zero-shot settings by recovering attributes such as identity, scene, gender, and race with 70-80% accuracy. Our code and data are available at https://github.com/williamium3000/ego-privacy.