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.
%0 Conference Paper
%1 Zerr:2012:PIC:2348283.2348292
%A Zerr, Sergej
%A Siersdorfer, Stefan
%A Hare, Jonathon
%A Demidova, Elena
%B Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
%C New York, NY, USA
%D 2012
%I ACM
%K aware classification cubric image iteco myown privacy search security webservice
%P 35--44
%R 10.1145/2348283.2348292
%T Privacy-aware image classification and search
%U http://doi.acm.org/10.1145/2348283.2348292
%X 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.
%@ 978-1-4503-1472-5
@inproceedings{Zerr:2012:PIC:2348283.2348292,
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.},
acmid = {2348292},
added-at = {2012-11-19T10:54:09.000+0100},
address = {New York, NY, USA},
author = {Zerr, Sergej and Siersdorfer, Stefan and Hare, Jonathon and Demidova, Elena},
biburl = {https://www.bibsonomy.org/bibtex/2a75198161d007f015af7180c512c1989/zerr},
booktitle = {Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval},
description = {Privacy-aware image classification and search},
doi = {10.1145/2348283.2348292},
interhash = {3ed4c2f96289adf184e1a993e9e0f2f0},
intrahash = {a75198161d007f015af7180c512c1989},
isbn = {978-1-4503-1472-5},
keywords = {aware classification cubric image iteco myown privacy search security webservice},
location = {Portland, Oregon, USA},
numpages = {10},
pages = {35--44},
publisher = {ACM},
series = {SIGIR '12},
timestamp = {2015-02-24T08:51:20.000+0100},
title = {Privacy-aware image classification and search},
url = {http://doi.acm.org/10.1145/2348283.2348292},
year = 2012
}