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Privacy-aware image classification and search

Published: 12 August 2012 Publication History

Abstract

Modern content sharing environments such as Flickr or YouTube contain a large amount of private resources such as photos showing weddings, family holidays, and private parties. These resources can be of a highly sensitive nature, disclosing many details of the users' private sphere. In order to support users in making privacy decisions in the context of image sharing and to provide them with a better overview on privacy related visual content available on the Web, we propose techniques to automatically detect private images, and to enable privacy-oriented image search. To this end, we learn privacy classifiers trained on a large set of manually assessed Flickr photos, combining textual metadata of images with a variety of visual features. We employ the resulting classification models for specifically searching for private photos, and for diversifying query results to provide users with a better coverage of private and public content. Large-scale classification experiments reveal insights into the predictive performance of different visual and textual features, and a user evaluation of query result rankings demonstrates the viability of our approach.

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      cover image ACM Conferences
      SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
      August 2012
      1236 pages
      ISBN:9781450314725
      DOI:10.1145/2348283
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 12 August 2012

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      Author Tags

      1. classification
      2. diversification
      3. image analysis
      4. privacy

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      • (2024)Intelligent System for Detection of Copyright-Protected Data for Enhanced Data SecurityBritish Journal of Computer, Networking and Information Technology10.52589/BJCNIT-OQQNPPCJ7:4(58-80)Online publication date: 17-Oct-2024
      • (2024)DIPA2Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314397:4(1-30)Online publication date: 12-Jan-2024
      • (2024)EFVAE: Efficient Federated Variational Autoencoder for Collaborative FilteringProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679818(3176-3185)Online publication date: 21-Oct-2024
      • (2024)Image sensitive identification and protection based on semantic segmentationThird International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)10.1117/12.3031222(213)Online publication date: 19-Jul-2024
      • (2024)HN3SInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10358061:2Online publication date: 1-Mar-2024
      • (2023)Deep Gated Multi-modal Fusion for Image Privacy PredictionACM Transactions on the Web10.1145/360844617:4(1-24)Online publication date: 10-Oct-2023
      • (2023)Raising User Awareness about the Consequences of Online Photo SharingProceedings of the 2023 ACM International Conference on Multimedia Retrieval10.1145/3591106.3592290(10-19)Online publication date: 12-Jun-2023
      • (2023)DIPA : An Image Dataset with Cross-cultural Privacy Concern AnnotationsCompanion Proceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581754.3584176(259-266)Online publication date: 27-Mar-2023
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