Automatic privacy classification of personal photos

Abstract

Tagging photos with privacy-related labels, such as “myself”, “friends” or “public”, allows users to selectively display pictures appropriate in the current situation (e. g. on the bus) or for specific groups (e. g. in a social network). However, manual labelling is time-consuming or not feasible for large collections. Therefore, we present an approach to automatically assign photos to privacy classes. We further demonstrate a study method to gather relevant image data without violating participants’ privacy. In a field study with 16 participants, each user assigned 150 personal photos to self-defined privacy classes. Based on this data, we show that a machine learning approach extracting easily available metadata and visual features can assign photos to user-defined privacy classes with a mean accuracy of 79. 38%.