Robustness Analysis of Face Obscuration
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2019.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 0; 2025: 1.
5 papers.
- 2021DeepBlur: A Simple and Effective Method for Natural Image Obfuscationcited by 13
- 2022Fantomas: Understanding Face Anonymization Reversibilitycited by 4
- 2021Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Videocited by 3
- 2022StyleGAN as a Utility-Preserving Face De-identification Methodcited by 2
- 2025FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methodscited by 0
- 2025FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods11 Mar 2025
- 2022StyleGAN as a Utility-Preserving Face De-identification Method5 Dec 2022
- 2022Fantomas: Understanding Face Anonymization Reversibility19 Oct 2022
- 2021Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video30 Aug 2021
- 2021DeepBlur: A Simple and Effective Method for Natural Image Obfuscation31 Mar 2021