GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents
It addresses location privacy risks for social media users by enhancing geolocation accuracy, though it is incremental as it builds on existing LVLMs with specialized tools.
The paper tackles the problem of image geolocation for privacy risk assessment by introducing GEO-Detective, an LLM-based agent that mimics human reasoning and tool use, achieving improvements of over 11.1% at the country level and reducing unknown predictions by more than 50.6% with external clues.
Images shared on social media often expose geographic cues. While early geolocation methods required expert effort and lacked generalization, the rise of Large Vision Language Models (LVLMs) now enables accurate geolocation even for ordinary users. However, existing approaches are not optimized for this task. To explore the full potential and associated privacy risks, we present Geo-Detective, an agent that mimics human reasoning and tool use for image geolocation inference. It follows a procedure with four steps that adaptively selects strategies based on image difficulty and is equipped with specialized tools such as visual reverse search, which emulates how humans gather external geographic clues. Experimental results show that GEO-Detective outperforms baseline large vision language models (LVLMs) overall, particularly on images lacking visible geographic features. In country level geolocation tasks, it achieves an improvement of over 11.1% compared to baseline LLMs, and even at finer grained levels, it still provides around a 5.2% performance gain. Meanwhile, when equipped with external clues, GEO-Detective becomes more likely to produce accurate predictions, reducing the "unknown" prediction rate by more than 50.6%. We further explore multiple defense strategies and find that Geo-Detective exhibits stronger robustness, highlighting the need for more effective privacy safeguards.