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English(EN) GeoCloak: An Agentic Framework for Semantics-Preserving Geolocation Privacy via Peripheral Clue Manipulation

新框架 GeoCloak 增强图像地理定位隐私

研究人员开发了 GeoCloak,一个新颖的代理框架,旨在保护图像中的地理定位隐私。该系统解决了大型视觉语言模型(LVLM)从照片推断位置的风险。GeoCloak 通过识别和抑制决定位置的关键线索(尤其是在图像视觉外围的线索)来工作,同时保持图像的整体语义和效用。评估表明,GeoCloak 在各种数据集和多个 LVLM 上显著降低了地理定位推断的准确性,优于现有方法。 AI

影响 增强用户对嵌入图像的位置数据的控制,减轻了来自先进 LVLM 的隐私风险。

排序理由 该集群包含一篇详细介绍新隐私保护框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架 GeoCloak 增强图像地理定位隐私

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该集群包含一篇详细介绍新隐私保护框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yixin Wu, Mingjie Li, Yun Shen, Michael Backes, Yang Zhang ·

    GeoCloak:一种通过外围线索操纵实现语义保持的地理定位隐私的代理框架

    arXiv:2610.20055v1 Announce Type: new Abstract: The rapid advancement of large vision language models (LVLMs) has made geolocation inference from images highly accurate and accessible. This poses serious privacy risks: a passive adversary with black-box LVLM access can infer a us…