Characterizing and Detecting Non-Consensual Photo Sharing on Social Networks

Photo capturing and sharing have become routine daily activities for social platform users. Alongside the entertainment of social interaction, we are experiencing tremendous visual violation and photo abusing. Especially, users may be unconsciously filmed and exposed online, which is termed as the non-consensual sharing issue. Unfortunately, this problem cannot be well handled with proactive access control or dedicated bystander detection, as users are unaware of their situations and may be filmed stealthily. We propose Videre on behalf of the privacy of the unaware parties in a way that they would be automatically identified and warned before such photos go public. For this, we first elaborate on the predominant features encountered in non-consensual captured photos via a thorough user study. Then we establish a dataset for this context and build a classifier as a proactive detector based on multi-deep-feature fusion. To relieve the burden of person-wise unawareness detection, we further design a signature-based filter for local pre-authorization, which can also implicitly avoid classification errors. We implement and test Videre in various field settings to demonstrate its effectiveness and performance.