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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

Indonesian

Untuk memahami mekanisme di balik lokalisasi halus, kami mengembangkan GeoTrace, sebuah serangan argumen terstruktur yang mengekspos bagaimana model mengekstrak petunjuk visual yang relevan dengan lokasi dan menyusunnya melalui alasan geografis kasar hingga halus, bahkan ketika metadata dan identifikasi lokasi geografis yang jelas dihapus. Untuk memahami mekanisme di balik lokalisasi halus seperti itu, kami mengembangkan GeoTrace, serangan argumen terstruktur yang mengekspos bagaimana model mengekstrak petunjuk visual yang relevan dengan lokasi dan menyusunnya melalui alasan geografis kasar hingga halus. Kami mengevaluasi GeoTrace pada target visual global dari 525 gambar biasa yang mencakup rantai petunjuk visual, perkotaan, dan manusia yang berpusat pada saat itu. Hasil kami menunjukkan bahwa GeoTrace mengekspos 25.71% dari gambar dalam 1 km dengan Gemini-3-Flash dan 16.95%

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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