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Graph-based neural networks for explainable image privacy inference

文献类型：期刊论文

作者Yang, Guang1,2; Cao, Juan1,2; Chen, Zhineng3; Guo, Junbo2; Li, Jintao2
刊名PATTERN RECOGNITION
出版日期2020-09-01
卷号105页码:12
ISSN号0031-3203
关键词Image privacy protection Graph neural networks Image classification
DOI10.1016/j.patcog.2020.107360
通讯作者Cao, Juan(caojuan@ict.ac.cn) ; Chen, Zhineng(zhineng.chen@ia.ac.cn)
英文摘要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.
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
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000539457100011
资助机构National Key Research and Development Program ; National Nature Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/40086]  
专题数字内容技术与服务研究中心_远程智能医疗
通讯作者Cao, Juan; Chen, Zhineng
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100086, 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.

入库方式： OAI收割

来源：自动化研究所

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