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.

翻译：随着社交平台的兴起，隐私保护已成为一个重要议题。隐私对象检测旨在精确定位图像中的隐私对象。它是数字时代保障个人隐私权和确保负责任数据处理实践的基础。由于对象的隐私性并非平移不变，隐私对象检测任务的本质在于基于场景信息推断对象隐私。然而，隐私对象检测长期以来被作为通用对象检测任务的子问题进行研究，因此现有方法在准确性、泛化性和可解释性方面存在严重不足。此外，由于法律限制，创建大规模隐私数据集十分困难，且现有隐私数据集缺乏标签粒度。现有隐私检测方法的粒度仍局限于图像级别。为解决上述两个问题，我们引入了两个用于对象级隐私检测的基准数据集，并提出了SHAN（场景异构图注意力网络）。该模型从图像构建场景异构图，并利用自注意力机制进行场景推理以获取对象隐私。通过实验，我们证明了SHAN在隐私对象检测任务中表现优异，所有指标均超过基线模型。