arXiv (All)
open access
AI Knows Where You Are: Exposure, Bias, and Inference in Multimodal Geolocation with KoreaGEO
Recent advances in vision-language models (VLMs) have enabled accurate image-based geolocation, raising serious concerns about location privacy risks in everyday social media posts. Yet, a systematic evaluation of such risks is still lacking: existing benchmarks show coarse granularity, linguistic b
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GeoAgent: Evaluating VLM Geolocalization Through Embodied Navigation#794264
- GeoAgent: Evaluating VLM Geolocalization Through Embodied Navigation#790245
- Hiding in Plain Sight: A Diffusion-based Mitigation of Geolocation Privacy Leakage in Vision-Language Models#1035769
- What Words Keep of a Place: Zero-Shot Language Reasoning for Cross-View Geo-Localization#1216644
- Neural Language Models’ sensitivity to long-distance dependencies and their island constraints is not universal: a comparison between English and Dutch humans and models#331597
Record · ID 1355906
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive