Details

Autor(en) / Beteiligte

• Yu, Jun
• Zhang, Baopeng
• Kuang, Zhengzhong
• Lin, Dan
• Fan, Jianping

Titel

iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning

Ist Teil von

• IEEE transactions on information forensics and security, 2017-05, Vol.12 (5), p.1005-1016

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• 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.