Faces are Protected as Privacy: An Automatic Tagging Framework Against Unpermitted Photo Sharing in Social Media

Abstract

On social platforms like Facebook, it is popular and pleasurable to share photos among friends, but it also puts other participants in the same picture in jeopardy when the photos are released online without the permission from them. To solve this problem, recently, the researchers have designed some fine-grained access control mechanisms for photos shared on the social platform. The uploader will tag each participant in the photo, then they will receive internal messages and configure their own privacy control strategies. These methods protect their privacy in photos by blurring out the faces of participants. However, there is still some defect in these strategies due to the unpredictable tagging behaviors of the uploader. Malicious users can easily manipulate unauthorized tagging processes and then publish the photos, which the participants want them to be confidential in social media. To address this critical problem, we propose a participant-free tagging system for photos on social platforms. This system excludes potential adversaries through automatic tagging processes over two cascading stages: 1) an initialization stage will be applied to every new user to collect his/her own portrait samples for future internal searching and tagging, and; 2) the remaining unidentified participants will be tagged in cooperative tagging stage by the users who have been identified in the first stage. For the system evaluation of efficiency and effectiveness, we conducted a series of experiments. The results demonstrated the tagging efficiency (96% tagging rate and 0.77s/photo tagging speed on average), photo masking and unmasking efficiency (0.13s/face on average), and the privacy preserving performance (over 90% identities in both group and individual photo are preserved).