Privacy Prediction of Images Shared on Social Media Sites Using Deep Features
This addresses privacy violations for social media users by improving prediction accuracy, though it is incremental as it builds on existing deep learning techniques.
The paper tackles the problem of predicting privacy settings for images shared on social media to prevent unwanted disclosure, and it shows that using deep features and deep image tags substantially outperforms baseline methods like SIFT, GIST, and bag of tags.
Online image sharing in social media sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. With the exponential increase in the number of images that are shared online every day, the development of effective and efficient prediction methods for image privacy settings are highly needed. The performance of models critically depends on the choice of the feature representation. In this paper, we present an approach to image privacy prediction that uses deep features and deep image tags as feature representations. Specifically, we explore deep features at various neural network layers and use the top layer (probability) as an auto-annotation mechanism. The results of our experiments show that models trained on the proposed deep features and deep image tags substantially outperform baselines such as those based on SIFT and GIST as well as those that use "bag of tags" as features.