Graph-based neural networks for explainable image privacy inference

doi:10.1016/j.patcog.2020.107360

	Graph-based neural networks for explainable image privacy inference
	Yang, Guang 1,2; Cao, Juan 1,2; Chen, Zhineng 3; Guo, Junbo 1; Li, Jintao 1
	2020-09-01
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
卷号	105 页码:12
摘要	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. (C) 2020 Elsevier Ltd. All rights reserved.
关键词	Image privacy protection Graph neural networks Image classification
DOI	10.1016/j.patcog.2020.107360
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program[2016YFB0800403] ; National Nature Science Foundation of China[U1703261]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000539457100011
出版者	ELSEVIER SCI LTD
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15107
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cao, Juan; Chen, Zhineng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100086, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Guang,Cao, Juan,Chen, Zhineng,et al. Graph-based neural networks for explainable image privacy inference[J]. PATTERN RECOGNITION,2020,105:12.
APA	Yang, Guang,Cao, Juan,Chen, Zhineng,Guo, Junbo,&Li, Jintao.(2020).Graph-based neural networks for explainable image privacy inference.PATTERN RECOGNITION,105,12.
MLA	Yang, Guang,et al."Graph-based neural networks for explainable image privacy inference".PATTERN RECOGNITION 105(2020):12.