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arXiv 2602.05023cs.CRcs.AI
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视觉-语言模型在位置披露中是否尊重上下文完整性？

Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?

Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）

Ruixin Yang, Ethan Mendes, Arthur Wang, James Hays, Sauvik Das, Wei Xu, Alan Ritter

备注：
Accepted by ICLR 2026. Code and data can be downloaded via https://github.com/99starman/VLM-GeoPrivacyBench
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AI总结：

本文研究了视觉-语言模型在位置披露中的隐私问题，指出现有模型在隐私保护方面存在不足，提出VLM-GEOPRIVACY基准以评估模型对上下文完整性的尊重程度。

摘要：

视觉-语言模型（VLMs）在图像地理定位方面已展现出强大的性能，这种能力在前沿多模态大推理模型（MLRMs）的推动下进一步增强。这带来了显著的隐私风险，因为这些广泛可用的模型可以被用来从随意共享的照片中推断出敏感位置，通常在街道级精度下，可能超越分享者同意或意图披露的详细程度。尽管最近的研究提出了对地理定位披露实施全面限制以应对这一风险，但这些措施无法区分有效的地理定位用途与恶意行为。相反，VLMs应通过推理图像内的元素来维护上下文完整性，以确定适当的信息披露水平，平衡隐私与效用。为了评估模型是否尊重上下文完整性，我们引入了VLM-GEOPRIVACY基准，该基准挑战VLMs解读潜在的社会规范和上下文线索，并确定适当的位置披露水平。我们对14个领先VLMs的评估显示，尽管这些模型能够精确地进行图像地理定位，但它们在人类隐私期望方面表现不佳。它们经常在敏感情境中过度披露，并且容易受到基于提示的攻击。我们的结果呼吁在多模态系统中引入新的设计原则，以整合基于上下文的隐私推理。

英文摘要：

Vision-language models (VLMs) have demonstrated strong performance in image geolocation, a capability further sharpened by frontier multimodal large reasoning models (MLRMs). This poses a significant privacy risk, as these widely accessible models can be exploited to infer sensitive locations from casually shared photos, often at street-level precision, potentially surpassing the level of detail the sharer consented or intended to disclose. While recent work has proposed applying a blanket restriction on geolocation disclosure to combat this risk, these measures fail to distinguish valid geolocation uses from malicious behavior. Instead, VLMs should maintain contextual integrity by reasoning about elements within an image to determine the appropriate level of information disclosure, balancing privacy and utility. To evaluate how well models respect contextual integrity, we introduce VLM-GEOPRIVACY, a benchmark that challenges VLMs to interpret latent social norms and contextual cues in real-world images and determine the appropriate level of location disclosure. Our evaluation of 14 leading VLMs shows that, despite their ability to precisely geolocate images, the models are poorly aligned with human privacy expectations. They often over-disclose in sensitive contexts and are vulnerable to prompt-based attacks. Our results call for new design principles in multimodal systems to incorporate context-conditioned privacy reasoning.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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