Privacy-preserving Reflection Rendering for Augmented Reality

Yiqin Zhao, Sheng Wei, Tian Guo

Abstract: When the virtual objects consist of reflective materials, the required lighting information to render such objects can consist of privacy-sensitive information outside the current camera view. In this paper, we show, for the first time, that accuracy-driven multi-view environment lighting can reveal out-of-camera scene information and compromise privacy. We present a simple yet effective privacy attack that extracts sensitive scene information such as human faces and text from rendered objects under several application scenarios. To defend against such attacks, we develop a novel IPC2S defense and a conditional R2 defense. Our IPC2S defense, combined with a generic lighting reconstruction method, preserves the scene geometry while obfuscating the privacy-sensitive information. As a proof-of-concept, we leverage existing OCR and face detection models to identify text and human faces from past camera observations and blur the color pixels associated with detected regions. We evaluate the visual quality impact of our defense by comparing rendered virtual objects to ones rendered with a generic multi-lighting reconstruction technique, ARKit, and R2 defense. Our visual and quantitative results demonstrate that our defense leads to structurally similar reflections with up to 0.98 SSIM score across various rendering scenarios while preserving sensitive information by reducing the automatic extraction success rate to at most 8.8%.