Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset

• URL: http://arxiv.org/abs/2507.13981v1
• Date: Fri, 18 Jul 2025 14:43:24 GMT
• Title: Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset
• Authors: Sara Abdulaziz, Giacomo D'Amicantonio, Egor Bondarev,
• Abstract summary: This paper presents a framework for evaluating visual privacy-protection methods across three dimensions: privacy, utility, and practicality.<n>It introduces HR-VISPR, a publicly available human-centric dataset with biometric, soft-biometric, and non-biometric labels to train an interpretable privacy metric.
• Score: 2.184775414778289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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