EgoPrivacy：你的第一人称摄像头泄露了你的什么信息？
EgoPrivacy: What Your First-Person Camera Says About You?
AI总结：
该研究聚焦可穿戴摄像头佩戴者的隐私泄露问题，构建了首个大规模自我中心视觉隐私风险评估基准EgoPrivacy，还提出检索增强攻击策略，证实佩戴者隐私信息极易被推断泄露。
摘要：
尽管可穿戴摄像头的快速普及引发了人们对自我中心视频隐私的重大担忧，但此前的研究在很大程度上忽略了摄像头佩戴者面临的独特隐私威胁。本研究探讨了一个核心问题：从第一人称视角视频中能推断出多少关于摄像头佩戴者的隐私信息？我们推出了EgoPrivacy，这是首个用于全面评估自我中心视觉隐私风险的大规模基准。EgoPrivacy涵盖三类隐私（人口统计信息、个人信息和情境信息），定义了七项任务，旨在恢复从细粒度（如佩戴者身份）到粗粒度（如年龄组）的各类隐私信息。为进一步凸显自我中心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，利用从外部第三人称视频库中进行的自我-第三人称检索，提升人口统计隐私攻击的效果。本文对所有威胁模型下可能出现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，我们的研究结果表明，即使在零样本设置下，基础模型也能有效侵犯佩戴者隐私，以70-80%的准确率恢复身份、场景、性别和种族等属性。我们的代码和数据可在https://github.com/williamium3000/ego-privacy获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
