  With the rise of social platforms, protecting privacy has become an important
issue. Privacy object detection aims to accurately locate private objects in
images. It is the foundation of safeguarding individuals' privacy rights and
ensuring responsible data handling practices in the digital age. Since privacy
of object is not shift-invariant, the essence of the privacy object detection
task is inferring object privacy based on scene information. However, privacy
object detection has long been studied as a subproblem of common object
detection tasks. Therefore, existing methods suffer from serious deficiencies
in accuracy, generalization, and interpretability. Moreover, creating
large-scale privacy datasets is difficult due to legal constraints and existing
privacy datasets lack label granularity. The granularity of existing privacy
detection methods remains limited to the image level. To address the above two
issues, we introduce two benchmark datasets for object-level privacy detection
and propose SHAN, Scene Heterogeneous graph Attention Network, a model
constructs a scene heterogeneous graph from an image and utilizes
self-attention mechanisms for scene inference to obtain object privacy. Through
experiments, we demonstrated that SHAN performs excellently in privacy object
detection tasks, with all metrics surpassing those of the baseline model.
