Publication Details
| Daniel Buschek, Moritz Bader, Emanuel von Zezschwitz, Alexander De Luca
Automatic Privacy Classification of Personal Photos In INTERACT '15: Proceedings of the 15th IFIP TC.13 International Conference on Human-Computer Interaction. Bamberg, Germany, September 14-18, 2015. (bib) |
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| 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%. |
