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Online image sharing in social media sites such as Facebook, Flickr, andInstagram can lead to unwanted disclosure and privacy violations, when privacysettings are used inappropriately. With the exponential increase in the numberof images that are shared online every day, the development of effective andefficient prediction methods for image privacy settings are highly needed. Theperformance of models critically depends on the choice of the featurerepresentation. In this paper, we present an approach to image privacyprediction that uses deep features and deep image tags as featurerepresentations. Specifically, we explore deep features at various neuralnetwork layers and use the top layer (probability) as an auto-annotationmechanism. The results of our experiments show that models trained on theproposed deep features and deep image tags substantially outperform baselinessuch as those based on SIFT and GIST as well as those that use "bag of tags" asfeatures.

Privacy Prediction of Images Shared on Social Media Sites Using Deep Features