2015/10/29 by Ashwini Tonge, Tonge, Ashwini, Cornelia Caragea +1
Computer Science · Social Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Face recognition and analysis #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #cs.CV #cs.CY
paper · pdf · doi:10.48550/arxiv.1510.08583
openalex publication_date 2015/10/29 · arxiv created 2015/11/05 · arxiv updated 2015/11/06 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.