Images today are increasingly shared online on social networking sites such as Facebook, Flickr, Foursquare, and Instagram. Image sharing occurs not only within a group of friends but also more and more outside a user's social circles for purposes of social discovery. Despite that current social networking sites allow users to change their privacy preferences, this is often a cumbersome task for the vast majority of users on the Web, who face difficulties in assigning and managing privacy settings. When these privacy settings are used inappropriately, online image sharing can potentially lead to unwanted disclosures and privacy violations. Thus, automatically predicting images' privacy to warn users about private or sensitive content before uploading these images on social networking sites has become a necessity in our current interconnected world.In this paper, we explore learning models to automatically predict appropriate images' privacy as private or public using carefully identified image-specific features. We study deep visual semantic features that are derived from various layers of Convolutional Neural Networks (CNNs) as well as textual features such as user tags and deep tags generated from deep CNNs. Particularly, we extract deep (visual and tag) features from four pre-trained CNN architectures for object recognition, i.e., AlexNet, GoogLeNet, VGG-16, and ResNet, and compare their performance for image privacy prediction. Among all four networks, we observe that ResNet produces the best feature representations for this task. We also fine-tune the pre-trained CNN architectures on our privacy dataset and compare their performance with the models trained on pre-trained features. The results show that even though the overall performance obtained using the fine-tuned networks is comparable to that of pre-trained networks, the fine-tuned networks provide an improved performance for the private class as compared to models trained on the pre-trained features. Results of our experiments on a Flickr dataset of over thirty thousand images show that the learning models trained on features extracted from ResNet outperform the state-of-the-art models for image privacy prediction. We further investigate the combination of user tags and deep tags derived from CNN architectures using two settings: (1) SVM on the bag-of-tags features; and (2) text-based CNN. We compare these models with the models trained on ResNet visual features obtained for privacy prediction. Our results show that even though the models trained

With millions of images that are shared online on social networking sites, e ective 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. Speci cally, our approach identi es the set of most competent modalities on the y, according to each new target image whose privacy has to be predicted. e approach considers three stages to predict the privacy of a target image, wherein we rst identify the neighborhood images that are visually similar and/or have similar sensitive content as the target image. en, 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-classi ers to obtain an optimal combination of modalities. CCS CONCEPTS•Security and privacy → Social network security and privacy; KEYWORDS Image privacy prediction; fusion of modalities; decision-level fusion ACM Reference format:

With the exponential increase in the number of images that are shared online every day, the development of effective and efficient learning methods for image privacy prediction has become crucial. Prior works have used as features automatically derived object tags from images' content and manually annotated user tags. However, we believe that in addition to objects, the scene context obtained from images’ content can improve the performance of privacy prediction. Hence, we propose to uncover scene-based tags from images' content using convolutional neural networks. Experimental results on a Flickr dataset show that the scene tags and object tags complement each other and yield the best performance when used in combination with user tags.

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, the development of effective and efficient prediction methods for image privacy settings are highly needed. In this study, we explore deep visual features and deep image tags for image privacy prediction. The results of our experiments show that models trained on deep visual features outperform those trained on SIFT and GIST. The results also show that deep image tags combined with user tags perform best among all tested features.

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