Privacy-aware Tag Recommendation for Accurate Image Privacy Prediction

Abstract

Online images’ tags are very important for indexing, sharing, and searching of images, as well as surfacing images with private or sensitive content, which needs to be protected. Social media sites such as Flickr generate these metadata from user-contributed tags. However, as the tags are at the sole discretion of users, these tags tend to be noisy and incomplete. In this article, we present a privacy-aware approach to automatic image tagging, which aims at improving the quality of user annotations, while also preserving the images’ original privacy sharing patterns. Precisely, we recommend potential tags for each target image by mining privacy-aware tags from the most similar images of the target image, which are obtained from a large collection. Experimental results show that, although the user-input tags compose noise, our privacy-aware approach is able to predict accurate tags that can improve the performance of a downstream application on image privacy prediction and outperforms an existing privacy-oblivious approach to image tagging. The results also show that, even for images that do not have any user tags, our proposed approach can recommend accurate tags. Crowd-sourcing the predicted tags exhibits the quality of our privacy-aware recommended tags. Our code, features, and the dataset used in experiments are available at: https://github.com/ashwinitonge/privacy-aware-tag-rec.git.