VisGuardian: A Lightweight Group-based Visual Privacy Control Technique For Smart Glasses in Home Environments
Abstract
Always-on sensing of AI applications on AR glasses makes traditional permission techniques inefficient for context-dependent private visual data within home environments. Home presents a challenging privacy context due to massive sensitive objects and the intimate nature of daily routines. 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 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.
Fields of Study & Subjects
Advanced Malware Detection Techniques
User Authentication and Security Systems
Adversarial Robustness in Machine Learning
Publication details
- Journal / source
- Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Pages
- 1–22
- Publication date
- 13 April 2026