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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
Source PublicationIEEE Trans. Inf. Forensic Secur.
Volume12Issue:5Pages:1005
AbstractTo 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.
SubtypeArticle
Department存储
DOI10.1109/TIFS.2016.2636090
Funding OrganizationNational 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]
Indexed BySCI
Funding OrganizationNational 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 IDWOS:000395869700001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.siom.ac.cn/handle/181231/28240
Collection高密度光存储技术实验室
Corresponding Authoryujun@hdu.edu.cn; bpzhang@bjtu.edu.cn; zkung@uncc.edu; lindan@mst.edu; jfan@uncc.edu
Affiliation中国科学院上海光学精密机械研究所
Recommended Citation
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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