A Group-Based Personalized Model for Image Privacy Classification and Labeling

A Group-Based Personalized Model for Image Privacy Classification and Labeling

Haoti Zhong, Anna Squicciarini, David Miller, Cornelia Caragea

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3952-3958. https://doi.org/10.24963/ijcai.2017/552

We address machine prediction of an individual's label (private or public) for a given image. This problem is difficult due to user subjectivity and inadequate labeled examples to train individual, personalized models. It is also time and space consuming to train a classifier for each user. We propose a Group-Based Personalized Model for image privacy classification in online social media sites, which learns a set of archetypical privacy models (groups), and associates a given user with one of these groups. Our system can be used to provide accurate ``early warnings'' with respect to a user's privacy awareness level.
Keywords:
Multidisciplinary Topics and Applications: AI&Security and Privacy
Multidisciplinary Topics and Applications: AI and Social Sciences