title: Granular Privacy Control for Geolocation with Vision Language Models
summary: 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.
paper_authors._id: 668c8a2d4587d0a82bb02b90
paper_authors.name: Ethan Mendes
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-09T08:15:27.239Z
paper_authors._id: 635d76ce94e5b275ca74b967
paper_authors.avatarUrl: /avatars/5d9a389e5fd558c0b8f0724bf0838a3e.svg
paper_authors.fullname: Ethan Mendes
paper_authors.type: user
paper_authors.user: emendes3
paper_authors._id: 668c8a2d4587d0a82bb02b91
paper_authors.name: Yang Chen
paper_authors._id: 668c8a2d4587d0a82bb02b92
paper_authors.name: James Hays
paper_authors._id: 668c8a2d4587d0a82bb02b93
paper_authors.name: Sauvik Das
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-07-09T18:26:17.065Z
paper_authors._id: 651487c3d0b4b4958c46eea2
paper_authors.avatarUrl: /avatars/f8850d1e66bd0c2364a2c664af4b631f.svg
paper_authors.fullname: Sauvik Das
paper_authors.type: user
paper_authors.user: scyrusk
paper_authors._id: 668c8a2d4587d0a82bb02b94
paper_authors.name: Wei Xu
paper_authors._id: 668c8a2d4587d0a82bb02b95
paper_authors.name: Alan Ritter
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-09T08:16:38.044Z
paper_authors._id: 6377da9065ce6b04bd493cf8
paper_authors.avatarUrl: /avatars/6be2632d6da49857928f8bcce8278f49.svg
paper_authors.fullname: Alan Ritter
paper_authors.type: user
paper_authors.user: rittera
paper_title: Granular Privacy Control for Geolocation with Vision Language Models
paper_summary: 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.
paper_ai_keywords: Vision Language Models (VLMs) image geolocators GPTGeoChat geolocation dialogues image geolocation conversations GPT-4v location information location granularity custom fine-tuned models prompted API-based models leaked location information country level city level supervised data