iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning
iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning
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DOI：
10.1109/tifs.2016.2636090
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发表时间：
2017-05-01
影响因子：
6.8
通讯作者：
Fan, Jianping
中科院分区：
文献类型：
--
作者：
Yu, Jun;Zhang, Baopeng;Fan, Jianping
为了实现图像共享的隐私设置的自动推荐，开发了一种称为iPrivacy（图像隐私）的新工具，用于释放用户在为特殊时刻共享图像时设置隐私偏好的负担。具体而言，本文的主要贡献包括：1）利用海量社交图像及其隐私设置，有效地学习对象与隐私的相关性，自动识别出一组隐私敏感的对象类; 2）开发了深度多任务学习算法，以联合学习更具代表性的深度卷积神经网络和更具鉴别力的树分类器，从而实现对大量隐私敏感对象类的快速准确检测; 3）可以通过从正在共享的图像中检测底层隐私敏感对象，识别它们的类别，并且根据对象-隐私相关性来识别它们的隐私设置;以及4）通过自动模糊隐私敏感对象来提供一种用于图像隐私保护的简单解决方案。我们已经进行了广泛的实验研究，对现实世界的图像和结果表明，我们所提出的方法的效率和有效性。
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.