PrivacyAlert: A Dataset for Image Privacy Prediction
Chenye Zhao, Jasmine Mangat, Sujay Koujalgi, Anna Squicciarini, Cornelia Caragea
Abstract
Image privacy issues have become an important challenge as millions of images are being shared on social networking sites every day. Often due to users' lack of privacy awareness and social pressure, users' posted images reveal sensitive information and may be easily used to their detriment. To address these issues, several recent studies have proposed machine learning models to automatically identify whether an image contains private information. However, progress on this important task has been hampered by the absence of reliable, publicly available, up-to-date datasets. To this end, we introduce PrivacyAlert, a dataset developed from recent images extracted from Flickr and annotated with privacy labels (private or public). Our data collection process is based on state-of-the-art privacy taxonomy and captures a comprehensive set of image types of various sensitivity. We perform a comprehensive analysis of our dataset and report image privacy prediction results using classic and deep learning models to set the ground for future studies. Our dataset is publicly available at: https://doi.org/10.5281/zenodo.6406870.