  Geographic privacy or geo-privacy refers to the keeping private of one's
geographic location, especially the restriction of geographical data maintained
by personal electronic devices. Geo-privacy is a crucial aspect of personal
security; however, it often goes unnoticed in daily activities. With the surge
in the use of Large Multimodal Models (LMMs), such as GPT-4, for Open Source
Intelligence (OSINT), the potential risks associated with geo-privacy breaches
have intensified. This study develops a location-integrated GPT-4 based model
named GeoLocator and designs four-dimensional experiments to demonstrate its
capability in inferring the locational information of input imageries and/or
social media contents. Our experiments reveal that GeoLocator generates
specific geographic details with high accuracy and consequently embeds the risk
of the model users exposing geospatial information to the public
unintentionally, highlighting the thread of online data sharing, information
gathering technologies and LLMs on geo-privacy. We conclude with the broader
implications of GeoLocator and our findings for individuals and the community
at large, by emphasizing the urgency for enhanced awareness and protective
measures against geo-privacy leakage in the era of advanced AI and widespread
social media usage.
