  With millions of images that are shared online on social networking sites,
effective methods for image privacy prediction are highly needed. In this
paper, we propose an approach for fusing object, scene context, and image tags
modalities derived from convolutional neural networks for accurately predicting
the privacy of images shared online. Specifically, our approach identifies the
set of most competent modalities on the fly, according to each new target image
whose privacy has to be predicted. The approach considers three stages to
predict the privacy of a target image, wherein we first identify the
neighborhood images that are visually similar and/or have similar sensitive
content as the target image. Then, we estimate the competence of the modalities
based on the neighborhood images. Finally, we fuse the decisions of the most
competent modalities and predict the privacy label for the target image.
Experimental results show that our approach predicts the sensitive (or private)
content more accurately than the models trained on individual modalities
(object, scene, and tags) and prior privacy prediction works. Also, our
approach outperforms strong baselines, that train meta-classifiers to obtain an
optimal combination of modalities.
