  Privacy is a complex, subjective and contextual concept that is difficult to
define. Therefore, the annotation of images to train privacy classifiers is a
challenging task. In this paper, we analyse privacy classification datasets and
the properties of controversial images that are annotated with contrasting
privacy labels by different assessors. We discuss suitable features for image
privacy classification and propose eight privacy-specific and
human-interpretable features. These features increase the performance of deep
learning models and, on their own, improve the image representation for privacy
classification compared with much higher dimensional deep features.
