DOI

Today's geo-location estimation approaches are able to infer the location of a target image using its visual content alone. These approaches typically exploit visual matching techniques, applied to a large collection of background images with known geo-locations. Users who are unaware that visual analysis and retrieval approaches can compromise their geo-privacy, unwittingly open themselves to risks of crime or other unintended consequences. This paper lays the groundwork for a new approach to geo-privacy of social images: Instead of requiring a change of user behavior, we start by investigating users' existing photo-sharing practices. We carry out a series of experiments using a large collection of social images (8.5M) to systematically analyze how photo editing practices impact the performance of geo-location estimation. We find that standard image enhancements, including filters and cropping, already serve as natural geo-privacy protectors. In our experiments, up to 19% of images whose location would otherwise be automatically predictable were unlocalizeable after enhancement. We conclude that it would be wrong to assume that geo-visual privacy is a lost cause in today's world of rapidly maturing machine learning. Instead, protecting users against the unwanted effects of pixel-based inference is a viable research field. A starting point is understanding the geo-privacy bonus of already established user behavior.

Original languageEnglish
Title of host publicationProceedings of the 2017 ACM International Conference on Multimedia Retrieval
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages84-92
Number of pages9
ISBN (Electronic)978-1-4503-4701-3
DOIs
Publication statusPublished - 2017
EventICMR 2017: ACM International Conference on Multimedia Retrieval - Bucharest, Romania
Duration: 6 Jun 20179 Jun 2017
http://www.icmr2017.ro/

Conference

ConferenceICMR 2017
CountryRomania
CityBucharest
Period6/06/179/06/17
Internet address

    Research areas

  • geo-privacy, geo-location estimation, usable privacy for multimedia retrieval

ID: 35663324