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.

利用从图像中提取的对象来确定图像为私人图像的难度比较大。通过使用特征归属来解释这些模型的决策，我们发现人类类别和其个数是决定隐私决策的主要因素。因此，这些模型在识别包含敏感数据的私人图像、车辆所有权和互联网活动的图像，以及含有人类的公共图像时通常失败。为了将来的基准测试，我们还设计了两个基于人类出现和数量的策略，并实现了与隐私模型相当的分类性能。