提出三维评估框架与人体隐私数据集，量化视觉隐私保护效果。
Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset
- 从隐私、效用、实用性三维度评估图像隐私技术
- 构建含生物特征标签的公开数据集HR-VISPR
- 适合研究隐私保护与人眼感知对齐的学者使用
AI驱动的监控技术发展加剧了对敏感个人数据收集与处理的担忧。为此，研究日益聚焦于隐私优先的设计方案，亟需客观的评估方法。本文提出一个涵盖隐私、效用与实用性的三维评估框架，并引入HR-VISPR——一个公开可用的人体中心数据集，包含生物特征、软生物特征及非生物特征标签，用于训练可解释的隐私度量模型。我们基于该框架评估了11种隐私保护方法，涵盖传统技术与先进深度学习方法。框架能有效区分与人类视觉感知一致的隐私等级，同时揭示隐私、效用与实用性之间的权衡关系。本研究与HR-VISPR数据集共同提供了一个结构化评估工具，适用于多种应用场景。
原文摘要 · Abstract (English)
Recent advances in AI-powered surveillance have intensified concerns over the collection and processing of sensitive personal data. In response, research has increasingly focused on privacy-by-design solutions, raising the need for objective techniques to evaluate privacy protection. This paper presents a comprehensive framework for evaluating visual privacy-protection methods across three dimensions: privacy, utility, and practicality. In addition, it introduces HR-VISPR, a publicly available human-centric dataset with biometric, soft-biometric, and non-biometric labels to train an interpretable privacy metric. We evaluate 11 privacy protection methods, ranging from conventional techniques to advanced deep-learning methods, through the proposed framework. The framework differentiates privacy levels in alignment with human visual perception, while highlighting trade-offs between privacy, utility, and practicality. This study, along with the HR-VISPR dataset, serves as an insightful tool and offers a structured evaluation framework applicable across diverse contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。