iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning
文献类型：期刊论文
| 作者 | Yu, Jun; Zhang, Baopeng; Kuang, Zhengzhong; Lin, Dan; Fan, Jianping |
| 刊名 | IEEE Trans. Inf. Forensic Secur.
 |
| 出版日期 | 2017 |
| 卷号 | 12期号:5页码:1005 |
| 通讯作者 | yujun@hdu.edu.cn ; bpzhang@bjtu.edu.cn ; zkung@uncc.edu ; lindan@mst.edu ; jfan@uncc.edu |
| 英文摘要 | 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. |
| 收录类别 | SCI |
| 资助信息 | 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 |
| 源URL | [http://ir.siom.ac.cn/handle/181231/28240]  |
| 专题 | 上海光学精密机械研究所_高密度光存储技术实验室 |
| 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.(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. |
入库方式： OAI收割
来源：上海光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。