  Today's geo-location estimation approaches are able to infer the location of
a target image using its visual content alone. These approaches exploit visual
matching techniques, applied to a large collection of background images with
known geo-locations. Users who are unaware that visual retrieval approaches can
compromise their geo-privacy, unwittingly open themselves to risks of crime or
other unintended consequences. Private photo sharing is not able to protect
users effectively, since its inconvenience is a barrier to consistent use, and
photos can still fall into the wrong hands if they are re-shared. This paper
lays the groundwork for a new approach to geo-privacy of social images: Instead
of requiring a complete change of user behavior, we investigate the protection
potential latent in users existing practices. We carry out a series of
retrieval experiments using a large collection of social images (8.5M) to
systematically analyze where users should be wary, and how both photo taking
and editing practices impact the performance of geo-location estimation. We
find that practices that are currently widespread are already sufficient to
protect single-handedly the geo-location ('geo-cloak') up to more than 50% of
images whose location would otherwise be automatically predictable. Our
conclusion is that protecting users against the unwanted effects of visual
retrieval is a viable research field, and should take as its starting point
existing user practices.
