Real-Time Access Control for Background and Co-Occurrence Image Privacy Protection

In today's digital age, the proliferation of social networks and advanced camera technology has led to countless images being shared on online social platforms daily, potentially resulting in significant breaches of personal privacy. In recent years, many methods have been proposed to protect image privacy, allowing users to be notified of potential privacy leaks before publishing their photos. However, most existing research primarily addresses the privacy protection of image owners or co-owners, while neglecting the privacy of people who appear in the background of others' images or who are co-occurring with others in the same image. In this paper, we propose a system capable of conducting real-time access control for protecting privacy of every individual appearing in a photo, as well as the privacy of people who co-occur in the same image. Specifically, we first detect all the faces in the image, then use a facial recognition algorithm to identify the corresponding users' privacy policies, and finally determine whether the image violates any user's privacy policy. In order to provide real-time access control, we have designed a facial attribute index tree to speed up the process of user identification. The experimental results show that compared with the method without our proposed index tree, our approach improves the time efficiency by almost two orders of magnitude while maintaining the accuracy of more than 97%.