外文题目: iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning
作者: Yu, Jun; Zhang, Baopeng; Kuang, Zhengzhong; Lin, Dan; Fan, Jianping
刊名: IEEE Trans. Inf. Forensic Secur.
年: 2017 卷: 12 期: 5 页： 1005--1016
英文关键词:
Image sharing; privacy setting recommendation; object-privacy alignment; image privacy protection; privacysensitive object classes; deep multi-task learning; tree classifier for hierarchical object detection
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.


文献类型: 期刊论文
正文语种: English
收录类别: SCI
DOI: 10.1109/TIFS.2016.2636090


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