Evaluation of Geolocation Capabilities of Multimodal Large Language Models and Analysis of Associated Privacy Risks

• URL: http://arxiv.org/abs/2506.23481v1
• Date: Mon, 30 Jun 2025 03:05:30 GMT
• Title: Evaluation of Geolocation Capabilities of Multimodal Large Language Models and Analysis of Associated Privacy Risks
• Authors: Xian Zhang, Xiang Cheng,
• Abstract summary: MLLMs are capable of inferring the geographic location of images based solely on visual content.<n>This poses serious risks of privacy invasion, including doxxing, surveillance, and other security threats.<n>The most advanced visual models can successfully localize the origin of street-level imagery with up to $49%$ accuracy within a 1-kilometer radius.
• Score: 9.003350058345442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objectives: The rapid advancement of Multimodal Large Language Models (MLLMs) has significantly enhanced their reasoning capabilities, enabling a wide range of intelligent applications. However, these advancements also raise critical concerns regarding privacy and ethics. MLLMs are now capable of inferring the geographic location of images -- such as those shared on social media or captured from street views -- based solely on visual content, thereby posing serious risks of privacy invasion, including doxxing, surveillance, and other security threats. Methods: This study provides a comprehensive analysis of existing geolocation techniques based on MLLMs. It systematically reviews relevant litera-ture and evaluates the performance of state-of-the-art visual reasoning models on geolocation tasks, particularly in identifying the origins of street view imagery. Results: Empirical evaluation reveals that the most advanced visual large models can successfully localize the origin of street-level imagery with up to $49\%$ accuracy within a 1-kilometer radius. This performance underscores the models' powerful capacity to extract and utilize fine-grained geographic cues from visual data. Conclusions: Building on these findings, the study identifies key visual elements that contribute to suc-cessful geolocation, such as text, architectural styles, and environmental features. Furthermore, it discusses the potential privacy implications associated with MLLM-enabled geolocation and discuss several technical and policy-based coun-termeasures to mitigate associated risks. Our code and dataset are available at https://github.com/zxyl1003/MLLM-Geolocation-Evaluation.

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