Robustness Analysis of Face Obscuration
| Autor: | Hanxiang Hao, Amy R. Reibman, David Guera, Edward J. Delp, János Horváth |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Information privacy Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Law enforcement 02 engineering and technology 010501 environmental sciences Computer security computer.software_genre 01 natural sciences Facial recognition system Identification (information) Robustness (computer science) Vulnerability assessment Face (geometry) Threat model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | FG |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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