Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908455663566848 |
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| author | Abdulaziz, Sara D'Amicantonio, Giacomo Bondarev, Egor |
| author_facet | Abdulaziz, Sara D'Amicantonio, Giacomo Bondarev, Egor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset Abdulaziz, Sara D'Amicantonio, Giacomo Bondarev, Egor Computer Vision and Pattern Recognition 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. |
| title | Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.13981 |