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iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning
Yu, Jun; Zhang, Baopeng; Kuang, Zhengzhong; Lin, Dan; Fan, Jianping; yujun@hdu.edu.cn; bpzhang@bjtu.edu.cn; zkung@uncc.edu; lindan@mst.edu; jfan@uncc.edu
2017
发表期刊IEEE Trans. Inf. Forensic Secur.
卷号12期号:5页码:1005
摘要To achieve automatic recommendation of privacy settings for image sharing, a new tool called iPrivacy (image privacy) is developed for releasing the burden from users on setting the privacy preferences when they share their images for special moments. Specifically, this paper consists of the following contributions: 1) massive social images and their privacy settings are leveraged to learn the object-privacy relatedness effectively and identify a set of privacy-sensitive object classes automatically; 2) a deep multi-task learning algorithm is developed to jointly learn more representative deep convolutional neural networks and more discriminative tree classifier, so that we can achieve fast and accurate detection of large numbers of privacy-sensitive object classes; 3) automatic recommendation of privacy settings for image sharing can be achieved by detecting the underlying privacy-sensitive objects from the images being shared, recognizing their classes, and identifying their privacy settings according to the object-privacy relatedness; and 4) one simple solution for image privacy protection is provided by blurring the privacy-sensitive objects automatically. We have conducted extensive experimental studies on real-world images and the results have demonstrated both the efficiency and effectiveness of our proposed approach.
文章类型Article
部门归属存储
DOI10.1109/TIFS.2016.2636090
资助者National Science Foundation [1651166-CNS, 1651455-CNS]; National Natural Science Foundation of China [61622205]; Zhejiang Provincial Natural Science Foundation of China [LR15F020002] ; National Science Foundation [1651166-CNS, 1651455-CNS]; National Natural Science Foundation of China [61622205]; Zhejiang Provincial Natural Science Foundation of China [LR15F020002] ; National Science Foundation [1651166-CNS, 1651455-CNS]; National Natural Science Foundation of China [61622205]; Zhejiang Provincial Natural Science Foundation of China [LR15F020002] ; National Science Foundation [1651166-CNS, 1651455-CNS]; National Natural Science Foundation of China [61622205]; Zhejiang Provincial Natural Science Foundation of China [LR15F020002]
收录类别SCI
资助者National Science Foundation [1651166-CNS, 1651455-CNS]; National Natural Science Foundation of China [61622205]; Zhejiang Provincial Natural Science Foundation of China [LR15F020002] ; National Science Foundation [1651166-CNS, 1651455-CNS]; National Natural Science Foundation of China [61622205]; Zhejiang Provincial Natural Science Foundation of China [LR15F020002] ; National Science Foundation [1651166-CNS, 1651455-CNS]; National Natural Science Foundation of China [61622205]; Zhejiang Provincial Natural Science Foundation of China [LR15F020002] ; National Science Foundation [1651166-CNS, 1651455-CNS]; National Natural Science Foundation of China [61622205]; Zhejiang Provincial Natural Science Foundation of China [LR15F020002]
WOS记录号WOS:000395869700001
引用统计
被引频次:57[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.siom.ac.cn/handle/181231/28240
专题高密度光存储技术实验室
通讯作者yujun@hdu.edu.cn; bpzhang@bjtu.edu.cn; zkung@uncc.edu; lindan@mst.edu; jfan@uncc.edu
作者单位中国科学院上海光学精密机械研究所
推荐引用方式
GB/T 7714
Yu, Jun,Zhang, Baopeng,Kuang, Zhengzhong,et al. iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning[J]. IEEE Trans. Inf. Forensic Secur.,2017,12(5):1005.
APA Yu, Jun.,Zhang, Baopeng.,Kuang, Zhengzhong.,Lin, Dan.,Fan, Jianping.,...&jfan@uncc.edu.(2017).iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning.IEEE Trans. Inf. Forensic Secur.,12(5),1005.
MLA Yu, Jun,et al."iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning".IEEE Trans. Inf. Forensic Secur. 12.5(2017):1005.
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