Image sharing

Artificial intelligence

Classifier (UML)

Discriminative model

Image (mathematics)

Social media

Computer science

World Wide Web

Trustworthiness

Information retrieval

Social network (sociolinguistics)

User-generated content

Internet privacy

IEEE Transactions on Information Forensics and Security

To configure successful privacy settings for social image sharing, two issues are inseparable: 1) content sensitiveness of the images being shared; and 2) trustworthiness of the users being granted to see the images. This paper aims to consider these two inseparable issues simultaneously to recommend fine-grained privacy settings for social image sharing. For achieving more compact representation of image content sensitiveness (privacy), two approaches are developed: 1) a deep network is adapted to extract 1024-D discriminative deep features; and 2) a deep multiple instance learning algorithm is adopted to identify 280 privacy-sensitive object classes and events. Second, users on the social network are clustered into a set of representative social groups to generate a discriminative dictionary for user trustworthiness characterization. Finally, both the image content sensitiveness and the user trustworthiness are integrated to train a tree classifier to recommend fine-grained privacy settings for social image sharing. Our experimental studies have demonstrated both the efficiency and the effectiveness of our proposed algorithms.

Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image Sharing