Graph-based neural networks for explainable image privacy inference | |
Yang, Guang1,2; Cao, Juan1,2; Chen, Zhineng3; Guo, Junbo1; Li, Jintao1 | |
发表期刊 | PATTERN RECOGNITION |
ISSN | 0031-3203 |
2020-09-01 | |
卷号 | 105期号:0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] |
项目资助者 | National Key Research and Development Program ; National Nature Science Foundation of China |
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 |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40087 |
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.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 |
通讯作者单位 | 中国科学院自动化研究所 |
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(0):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(0),12. |
MLA | Yang, Guang,et al."Graph-based neural networks for explainable image privacy inference".PATTERN RECOGNITION 105.0(2020):12. |
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