Granular Privacy Control for Geolocation with Vision Language Models
Vision Language Models (VLMs) are rapidly advancing in their capability to
answer information-seeking questions. As these models are widely deployed in
consumer applications, they could lead to new privacy risks due to emergent
abilities to identify people in photos, geolocate images, etc. As we
demonstrate, somewhat surprisingly, current open-source and proprietary VLMs
are very capable image geolocators, making widespread geolocation with VLMs an
immediate privacy risk, rather than merely a theoretical future concern. As a
first step to address this challenge, we develop a new benchmark, GPTGeoChat,
to test the ability of VLMs to moderate geolocation dialogues with users. We
collect a set of 1,000 image geolocation conversations between in-house
annotators and GPT-4v, which are annotated with the granularity of location
information revealed at each turn. Using this new dataset, we evaluate the
ability of various VLMs to moderate GPT-4v geolocation conversations by
determining when too much location information has been revealed. We find that
custom fine-tuned models perform on par with prompted API-based models when
identifying leaked location information at the country or city level; however,
fine-tuning on supervised data appears to be needed to accurately moderate
finer granularities, such as the name of a restaurant or building.
668c8a2d4587d0a82bb02b90
Ethan Mendes
admin_assigned
2024-07-09T08:15:27.239Z
635d76ce94e5b275ca74b967
/avatars/5d9a389e5fd558c0b8f0724bf0838a3e.svg
Ethan Mendes
user
emendes3
668c8a2d4587d0a82bb02b91
Yang Chen
668c8a2d4587d0a82bb02b92
James Hays
668c8a2d4587d0a82bb02b93
Sauvik Das
extracted_confirmed
2024-07-09T18:26:17.065Z
651487c3d0b4b4958c46eea2
/avatars/f8850d1e66bd0c2364a2c664af4b631f.svg
Sauvik Das
user
scyrusk
668c8a2d4587d0a82bb02b94
Wei Xu
668c8a2d4587d0a82bb02b95
Alan Ritter
admin_assigned
2024-07-09T08:16:38.044Z
6377da9065ce6b04bd493cf8
/avatars/6be2632d6da49857928f8bcce8278f49.svg
Alan Ritter
user
rittera
Granular Privacy Control for Geolocation with Vision Language Models
Vision Language Models (VLMs) are rapidly advancing in their capability to
answer information-seeking questions. As these models are widely deployed in
consumer applications, they could lead to new privacy risks due to emergent
abilities to identify people in photos, geolocate images, etc. As we
demonstrate, somewhat surprisingly, current open-source and proprietary VLMs
are very capable image geolocators, making widespread geolocation with VLMs an
immediate privacy risk, rather than merely a theoretical future concern. As a
first step to address this challenge, we develop a new benchmark, GPTGeoChat,
to test the ability of VLMs to moderate geolocation dialogues with users. We
collect a set of 1,000 image geolocation conversations between in-house
annotators and GPT-4v, which are annotated with the granularity of location
information revealed at each turn. Using this new dataset, we evaluate the
ability of various VLMs to moderate GPT-4v geolocation conversations by
determining when too much location information has been revealed. We find that
custom fine-tuned models perform on par with prompted API-based models when
identifying leaked location information at the country or city level; however,
fine-tuning on supervised data appears to be needed to accurately moderate
finer granularities, such as the name of a restaurant or building.
Current Vision Language Models can geolocate images effectively, posing immediate privacy risks, and a new benchmark, GPTGeoChat, is developed to evaluate their capability to moderate location information disclosure in dialogues.
vision language models VLMs image geolocation privacy risks benchmark GPTGeoChat location information disclosure geolocation conversations fine-tuning API-based models