Newest papers

Not Where You Think: Protecting Location Privacy Against Reasoning-Based Geolocation

Shanghao Shi, Jian Xiao, Hao Li, Yuanhaur Chang, Yevgeniy Vorobeychik, Nathan Jacobs, Neal Patwari, Ning Zhang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30439 · cited by 0 works (OpenAlex) · open-access PDF

French

Pour comprendre le mécanisme derrière une telle localisation fine-grain, nous développons GeoTrace, une attaque de raisonnement structurée qui expose la façon dont les modèles extraient des indices visuels pertinents pour la localisation et les composent à travers un raisonnement géographique brut à fin, même lorsque les métadonnées et les identifiants géographiques évidents sont supprimés. Nous évaluons GeoTrace sur un critère visuel mondial de 525 images ordinaires couvrant la chaîne de indices visuels naturels, urbains, résidentiels et centrés sur l'homme, tout en excluant les sites célèbres et les indicateurs de localisation directs. Nos résultats montrent que GeoTrace localise 25.71% des images dans 1 km avec Gemini-3-Flash et

Machine translation (Meta M2M100); the English original follows.

Abstract

Recent large reasoning models can infer where ordinary photos were taken using only visual information, creating a serious location privacy risk even when metadata and obvious geographic identifiers are removed. To understand the mechanism behind such fine-grained localization, we develop GeoTrace, a structured reasoning attack that exposes how models extract location-relevant visual clues and compose them through coarse-to-fine geographic reasoning. We evaluate GeoTrace on a worldwide benchmark of 525 ordinary images spanning natural, urban, residential, and human-centered scenes while excluding famous landmarks and direct location indicators. Our results show that GeoTrace localizes 25.71% of images within 1 km with Gemini-3-Flash and 16.95% with GPT-5-mini. More importantly, our analysis reveals a key insight in the visual clue chain: broad scene properties mainly support coarse localization, whereas precise localization disproportionately relies on small, distinctive, and editable local clues. Building on this insight, we develop GeoShift, a proactive user-side defense that breaks the visual clue chain before image release. Rather than simply removing geographic evidence, GeoShift preserves stable scene properties, selects a visually compatible false target location, and strategically rewrites high-impact geo-revealing clues to construct a coherent alternative geographic hypothesis. Experiments show that GeoShift reduces street-level localization from 16.95% to 2.48% under GPT-5-mini and from 25.71% to 3.81% under Gemini-3-Flash, while preserving visual quality and remaining effective against adaptive attacks.

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Topics: Geolocation, Computer science, Metadata, Artificial intelligence, Computer vision, Construct (python library)

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