Graph-based neural networks for explainable image privacy inference. (September 2020)

• Record Type: Journal Article
• Authors: Yang, Guang; Cao, Juan; Chen, Zhineng; Guo, Junbo; Li, Jintao
• Is Part Of: Pattern recognition. Volume 105(2020:Sep.)
• Publication Date: 2020-09
• Subjects: Image privacy protection -- Graph neural networks -- Image classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107360
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose an improved dataset for research on image privacy. We build a knowledge graph that represents the relevance between the objects and image privacy. Based on the graph, we deal with the traditional image classification problem with the novel graph-based neural network. The introduction of the knowledge graph not only makes the model more explainable but also makes better use of the information of objects provided by the images. Our method relies on the intrinsic relevance in the dataset rather than extra knowledge, thus is applicable for other tasks. 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.