  Accurately predicting whether an image is private before sharing it online is
difficult due to the vast variety of content and the subjective nature of
privacy itself. In this paper, we evaluate privacy models that use objects
extracted from an image to determine why the image is predicted as private. To
explain the decision of these models, we use feature-attribution to identify
and quantify which objects (and which of their features) are more relevant to
privacy classification with respect to a reference input (i.e., no objects
localised in an image) predicted as public. We show that the presence of the
person category and its cardinality is the main factor for the privacy
decision. Therefore, these models mostly fail to identify private images
depicting documents with sensitive data, vehicle ownership, and internet
activity, or public images with people (e.g., an outdoor concert or people
walking in a public space next to a famous landmark). As baselines for future
benchmarks, we also devise two strategies that are based on the person presence
and cardinality and achieve comparable classification performance of the
privacy models.
