AutoPrivacy: Automatic privacy protection and tagging suggestion for mobile social photo

With the increasing computing and storage capabilities, smart mobile devices are changing our daily lives and are emerging as the dominant computing platform for end-users. It is popular among the mobile users to take photos including selfies whenever and wherever they like, and further the captured photos are shared to their friends through social networks such as Facebook and WeChat. However, an increasing issue with the large number of photos taken by a mobile user is local photo management, e.g., image searching among the photos without image tags. Another issue is that photo sharing on social networks may infringe on the privacy of the unintended human objects in the images. In this paper, an automatic privacy protection and tag suggestion system, AutoPrivacy, is proposed for mobile social images. In particular, AutoPrivacy attempts to exploit sensors signatures, image processing, and the recognition model to achieve automatic privacy protection for the unintended human objects and tagging suggestion for the intended human objects. We utilize public album data of volunteers from Facebook to test the proposed automatic system, and the experimental results on an Android platform show that AutoPrivacy can perform real time detection of intended/unintended human objects and in turn provide accurate privacy protection for unintended human objects, while the tagging suggestion for the intended human objects is efficient requiring less additional storage.