  With an increasing number of users sharing information online, privacy
implications entailing such actions are a major concern. For explicit content,
such as user profile or GPS data, devices (e.g. mobile phones) as well as web
services (e.g. Facebook) offer to set privacy settings in order to enforce the
users' privacy preferences. We propose the first approach that extends this
concept to image content in the spirit of a Visual Privacy Advisor. First, we
categorize personal information in images into 68 image attributes and collect
a dataset, which allows us to train models that predict such information
directly from images. Second, we run a user study to understand the privacy
preferences of different users w.r.t. such attributes. Third, we propose models
that predict user specific privacy score from images in order to enforce the
users' privacy preferences. Our model is trained to predict the user specific
privacy risk and even outperforms the judgment of the users, who often fail to
follow their own privacy preferences on image data.
