# Robustness Analysis of Face Obscuration

arXiv: https://arxiv.org/abs/1905.05243 · HTML: https://mlsanity.com/paper/1905.05243/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

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

- [DeepBlur: A Simple and Effective Method for Natural Image Obfuscation](https://mlsanity.com/paper/2104.02655/) (2021)
- [Fantomas: Understanding Face Anonymization Reversibility](https://mlsanity.com/paper/2210.10651/) (2022)
- [Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video](https://mlsanity.com/paper/2108.13141/) (2021)
- [StyleGAN as a Utility-Preserving Face De-identification Method](https://mlsanity.com/paper/2212.02611/) (2022)
- [FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods](https://mlsanity.com/paper/2503.08731/) (2025)

## Abstract (arXiv)

Face obscuration is needed by law enforcement and mass media outlets to guarantee privacy. Sharing sensitive content where obscuration or redaction techniques have failed to completely remove all identifiable traces can lead to many legal and social issues. Hence, we need to be able to systematically measure the face obscuration performance of a given technique. In this paper we propose to measure the effectiveness of eight obscuration techniques. We do so by attacking the redacted faces in three scenarios: obscured face identification, verification, and reconstruction. Threat modeling is also considered to provide a vulnerability analysis for each studied obscuration technique. Based on our evaluation, we show that the k-same based methods are the most effective.