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

arXiv: https://arxiv.org/abs/2507.13981 · HTML: https://mlsanity.com/paper/2507.13981/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Rethinking FID: Towards a Better Evaluation Metric for Image Generation](https://mlsanity.com/paper/2401.09603/) (2023)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images](https://mlsanity.com/paper/1703.10660/) (2017)
- [Live Face De-Identification in Video](https://mlsanity.com/paper/1911.08348/) (2019)
- [SPAct: Self-supervised Privacy Preservation for Action Recognition](https://mlsanity.com/paper/2203.15205/) (2022)
- [Face-Off: Adversarial Face Obfuscation](https://mlsanity.com/paper/2003.08861/) (2020)
- [TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection](https://mlsanity.com/paper/2308.11072/) (2023)
- [Realistic Full-Body Anonymization with Surface-Guided GANs](https://mlsanity.com/paper/2201.02193/) (2022)
- [Image Privacy Prediction Using Deep Neural Networks](https://mlsanity.com/paper/1903.03695/) (2019)
- [Obfuscation of Images via Differential Privacy: From Facial Images to General Images](https://mlsanity.com/paper/2102.11072/) (2021)
- [Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos](https://mlsanity.com/paper/2212.10682/) (2022)
- [Dynamic Deep Multi-modal Fusion for Image Privacy Prediction](https://mlsanity.com/paper/1902.10796/) (2019)
- [Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images](https://mlsanity.com/paper/2303.10435/) (2023)
- [Privacy-Protection Drone Patrol System based on Face Anonymization](https://mlsanity.com/paper/2005.14390/) (2020)
- [Privacy-Preserving Image Acquisition Using Trainable Optical Kernel](https://mlsanity.com/paper/2106.14577/) (2021)

## Built on by (most cited first, 5 of 5)

- [Rethinking Visual Privacy: A Compositional Privacy Risk Framework for Severity Assessment with VLMs](https://mlsanity.com/paper/2603.21573/) (2026)
- [Unsafe2Safe: Controllable Image Anonymization for Downstream Utility](https://mlsanity.com/paper/2603.28605/) (2026)
- [When Recovery Matters: The Blind Spot of Surrogate Privacy in MLLM Editing](https://mlsanity.com/paper/2606.07171/) (2026)
- [Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs](https://mlsanity.com/paper/2606.07175/) (2026)
- [Privacy-Aware Synthetic Video Benchmarking and Relational Evaluation for Worker-Under-Suspended-Load Detection](https://mlsanity.com/paper/2607.16351/) (2026)

## Abstract (arXiv)

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.