Abstract With the development of social media and smartphones, people share their daily lives via a large number of images, but the convince also raises a problem of privacy leakage. Therefore, effective methods are needed to infer the privacy risk of images and identify images that may disclose privacy. Several works have tried to solve this problem with deep learning models. However, we know little about how the models infer the privacy label of an image, thus it is not easy to understand why the image may disclose privacy. Inspired by recent research on graph neural networks, we introduce prior knowledge to the deep models to make the inference more explainable. We propose the Graph-based neural networks for Image Privacy (GIP) to infer the privacy risk of images. The GIP mainly focuses on objects in an image, and the knowledge graph is extracted from the objects in the dataset without reliance on extra knowledge. Experimental results show that the GIP achieves higher performance compared with the object-based methods and comparable performance even compared with the multi-modal fusion method. The results show that the introduction of the knowledge graph not only makes the deep model more explainable but also makes better use of the information of objects provided by the images. Combing the knowledge graph with deep learning is a promising way to help protect image privacy that is worth exploring.

中文翻译：

---

用于可解释图像隐私推理的基于图的神经网络

摘要 随着社交媒体和智能手机的发展，人们通过大量的图片分享日常生活，但也引发了隐私泄露的问题。因此，需要有效的方法来推断图像的隐私风险并识别可能泄露隐私的图像。有几项工作试图用深度学习模型解决这个问题。然而，我们对模型如何推断图像的隐私标签知之甚少，因此不容易理解为什么图像可能会泄露隐私。受最近图神经网络研究的启发，我们将先验知识引入深度模型，使推理更具可解释性。我们提出了基于图的图像隐私神经网络（GIP）来推断图像的隐私风险。GIP 主要关注图像中的对象，并且知识图是从数据集中的对象中提取的，不依赖于额外的知识。实验结果表明，与基于对象的方法相比，GIP 实现了更高的性能，甚至与多模态融合方法相比，性能也相当。结果表明，知识图谱的引入不仅使深度模型更具可解释性，而且更好地利用了图像提供的对象信息。将知识图谱与深度学习相结合是一种很有前途的方法来帮助保护图像隐私，值得探索。结果表明，知识图谱的引入不仅使深度模型更具可解释性，而且更好地利用了图像提供的对象信息。将知识图谱与深度学习相结合是一种很有前途的方法来帮助保护图像隐私，值得探索。结果表明，知识图谱的引入不仅使深度模型更具可解释性，而且更好地利用了图像提供的对象信息。将知识图谱与深度学习相结合是一种很有前途的方法来帮助保护图像隐私，值得探索。