Pre-Capture Privacy via Adaptive Single-Pixel Imaging

Yoko Sogabe, Shiori Sugimoto, Ayumi Matsumoto, Masaki Kitahara; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 9035-9044

As cameras become ubiquitous in our living environment invasion of privacy is becoming a significant concern. A common approach to privacy preservation is to remove personally identifiable information from a captured image but there is a risk of the original image being leaked. In this paper we propose a pre-capture privacy-aware imaging method that captures images from which the details of a pre-specified anonymized target have been eliminated. The proposed method applies a single-pixel imaging framework in which we introduce a feedback mechanism called an aperture pattern generator (APG). The introduced APG adaptively outputs the next aperture pattern to avoid sampling the anonymized target by using already acquired data as a clue. Furthermore the anonymized target can be set to any object without changing hardware. Except for the removed detailed features of the anonymized target the captured images are of comparable quality to those captured by a general camera and can be used for various computer vision applications. We target faces and license plates and experimentally show that the proposed method can capture clear images in which detailed features of the anonymized target are eliminated achieving both privacy and utility.