{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/image-privacy-prediction-using-deep-neural","title":"Image Privacy Prediction Using Deep Neural Networks","arxiv_id":"1903.03695","date":"2019-03-08","proceeding":null,"authors":["Ashwini Tonge","Cornelia Caragea"],"abstract":"Images today are increasingly shared online on social networking sites such\nas Facebook, Flickr, Foursquare, and Instagram. Despite that current social\nnetworking sites allow users to change their privacy preferences, this is often\na cumbersome task for the vast majority of users on the Web, who face\ndifficulties in assigning and managing privacy settings. Thus, automatically\npredicting images' privacy to warn users about private or sensitive content\nbefore uploading these images on social networking sites has become a necessity\nin our current interconnected world.\n  In this paper, we explore learning models to automatically predict\nappropriate images' privacy as private or public using carefully identified\nimage-specific features. We study deep visual semantic features that are\nderived from various layers of Convolutional Neural Networks (CNNs) as well as\ntextual features such as user tags and deep tags generated from deep CNNs.\nParticularly, we extract deep (visual and tag) features from four pre-trained\nCNN architectures for object recognition, i.e., AlexNet, GoogLeNet, VGG-16, and\nResNet, and compare their performance for image privacy prediction. Results of\nour experiments on a Flickr dataset of over thirty thousand images show that\nthe learning models trained on features extracted from ResNet outperform the\nstate-of-the-art models for image privacy prediction. We further investigate\nthe combination of user tags and deep tags derived from CNN architectures using\ntwo settings: (1) SVM on the bag-of-tags features; and (2) text-based CNN. Our\nresults show that even though the models trained on the visual features perform\nbetter than those trained on the tag features, the combination of deep visual\nfeatures with image tags shows improvements in performance over the individual\nfeature sets.","url_abs":"http://arxiv.org/abs/1903.03695v1","url_pdf":"http://arxiv.org/pdf/1903.03695v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"image-privacy-prediction-using-deep-neural","repo_url":"https://github.com/ashwinitonge/deepprivate","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"tag","task_name":"TAG"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"googlenet","method_name":"GoogLeNet"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"inception-module","method_name":"Inception Module"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"svm","method_name":"SVM"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}