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.

通过构建场景异构图和利用自注意机制对场景推理，提出了 SHAN（Scene Heterogeneous graph Attention Network）模型，用于目标级别隐私检测任务，实验证明 SHAN 在隐私对象检测任务中表现出色，所有指标均超过基准模型。