Always-on sensing of AI applications on AR glasses makes traditional permission techniques ill-suited for context-dependent visual data, especially within home environments. The home presents a highly challenging privacy context due to the high density of sensitive objects, and the frequent presence of non-consenting family members, and the intimate nature of daily routines, making it a critical focus area for scalable privacy control mechanisms. Existing fine-grained controls, while offering nuanced choices, are inefficient for managing multiple private objects. We propose VisGuardian, a fine-grained content-based visual permission technique for AR glasses. VisGuardian features a group-based control mechanism that enables users to efficiently manage permissions for multiple private objects. VisGuardian detects objects using YOLO and adopts a pre-classified schema to group them. By selecting a single object, users can efficiently obscure groups of related objects based on criteria including privacy sensitivity, object category, or spatial proximity. A technical evaluation shows VisGuardian achieves mAP50 of 0.6704 with only 14.0 ms latency and a 1.7% increase in battery consumption per hour. Furthermore, a user study (N=24) comparing VisGuardian to slider-based and object-based baselines found it to be significantly faster for setting permissions and was preferred by users for its efficiency, effectiveness, and ease of use.

翻译：增强现实眼镜上人工智能应用的持续感知特性，使得传统权限控制技术难以适应上下文相关的视觉数据，尤其在家庭环境中。家庭环境因敏感物体高度密集、非同意家庭成员频繁出现以及日常活动的私密性，构成了极具挑战性的隐私场景，使其成为可扩展隐私控制机制的关键研究领域。现有的细粒度控制虽能提供精细选择，但在管理多个隐私对象时效率低下。本文提出VisGuardian，一种面向增强现实眼镜的基于内容的细粒度视觉权限控制技术。VisGuardian采用分组控制机制，使用户能高效管理多个隐私对象的权限。该系统通过YOLO检测物体，并采用预分类模式进行对象分组。用户通过选择单个对象，即可根据隐私敏感度、物体类别或空间邻近性等标准，高效遮蔽相关对象群组。技术评估表明，VisGuardian实现了0.6704的mAP50指标，延迟仅为14.0毫秒，每小时电池消耗仅增加1.7%。此外，一项涉及24名用户的对比研究显示，相较于基于滑动条和基于单对象的基线方法，VisGuardian在权限设置速度上显著更快，并因其高效性、有效性和易用性获得用户青睐。