Content-based Graph Privacy Advisor

People may be unaware of the privacy risks of uploading an image online. In this paper, we present an image privacy classifier that uses scene information and object cardinality as cues for the prediction of image privacy. Our Graph Privacy Advisor (GPA) model simplifies a state-of-the-art graph model and improves its performance by refining the relevance of the content-based information extracted from the image. We determine the most informative visual features to be used for the privacy classification task and reduce the complexity of the model by replacing high-dimensional image-based feature vectors with lower-dimensional, more effective features. We also address the biased prior information by modelling object co-occurrences instead of the frequency of object occurrences in each class.