精细遮蔽隐私信息，保护视障用户图像隐私同时保留有用内容。
Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Users
- 结合细粒度分割与数据驱动风险评分，精准识别高危隐私区域。
- 相比粗粒度方法，保留26%更多图像内容，提升视觉模型理解能力45%。
- 适合关注图像隐私保护的AI系统开发者与无障碍应用设计者。
随着视觉语言模型（VLMs）驱动的视觉辅助系统日益普及，用户隐私问题愈发突出，尤其是视障用户在拍摄图像时可能无意中捕获个人隐私信息。现有隐私保护方法依赖粗粒度分割，对整个私密对象进行统一遮蔽，常牺牲可用性。本文提出FiGPriv，一种细粒度隐私保护框架，仅对高风险隐私信息进行选择性遮蔽，同时保留低风险信息。该方法融合细粒度分割与数据驱动的风险评分机制。我们在BIV-Priv-Seg数据集上评估，结果显示FiGPriv在确保隐私保护的前提下，保留了+26%的图像内容，使VLMs提供有用回应的能力提升11%，图像内容识别准确率提高45%。
原文摘要 · Abstract (English)
As visual assistant systems powered by visual language models (VLMs) become more prevalent, concerns over user privacy have grown, particularly for blind and low vision users who may unknowingly capture personal private information in their images. Existing privacy protection methods rely on coarse-grained segmentation, which uniformly masks entire private objects, often at the cost of usability. In this work, we propose FiGPriv, a fine-grained privacy protection framework that selectively masks only high-risk private information while preserving low-risk information. Our approach integrates fine-grained segmentation with a data-driven risk scoring mechanism. We evaluate our framework using the BIV-Priv-Seg dataset and show that FiG-Priv preserves +26% of image content, enhancing the ability of VLMs to provide useful responses by 11% and identify the image content by 45%, while ensuring privacy protection. Project Page: https://artcs1.github.io/VLMPrivacy/
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