The Geo-Privacy Bonus of Popular Photo Enhancements

Abstract

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.