Automatic Privacy Prediction to Accelerate Social Image Sharing

The manual process for privacy setting could be very time-consuming and challenging for common users. By assuming that there are hidden correlations between the visual properties of images (i.e., visual features) or object classes and the privacy settings for image sharing, an effective algorithm is developed in this paper to achieve automatic prediction of image privacy, so that the best-matching privacy setting can be recommended automatically for each single image being shared. Our algorithm for automatic image privacy prediction contains two approaches: (a) feature-based approach by learning more representative deep features and discriminative classifier for assigning each single image being shared into one of two categories: private vs. public, (b) object-based approach by detecting large numbers of privacy-sensitive object classes and events automatically and leveraging them to achieve more discriminative characterization of image privacy, so that we can support more explainable solution for automatic image privacy prediction. We have also conducted extensive experimental studies on large-scale social images, which have demonstrated both efficiency and effectiveness of our proposed algorithm.