Textdokument

Achieving Facial De-Identification by Taking Advantage of the Latent Space of Generative Adversarial Networks

Vorschaubild nicht verf√ľgbar
Volltext URI
Dokumententyp
Datum
2021
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Quelle
INFORMATIK 2021
Workshop: Security, Datenschutz und Anonymisierung
Verlag
Gesellschaft f√ľr Informatik, Bonn
Zusammenfassung
The General Data Protection Regulation (EU)2016/679 passed by the European Union prohibits any data collection and processing that was conducted without the consent of the individuals involved. Especially images showing faces are often subject to these regulations and therefore, either need to be removed or anonymized. Early approaches however were often troubled by strong visual artifacts. In this work, we propose a novel anonymization pipeline that generates a proxy face for a group of individuals by taking advantage of the semantics of the latent space of generative adversarial networks. Experiments have shown that by following a ūĚĎė-same approach and utilizing different clustering techniques, privacy for the individuals involved can be greatly enhanced, while preserving important facial characteristics.
Beschreibung
Frick, Raphael Antonius; Steinebach, Martin (2021): Achieving Facial De-Identification by Taking Advantage of the Latent Space of Generative Adversarial Networks. INFORMATIK 2021. DOI: 10.18420/informatik2021-068. Gesellschaft f√ľr Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-708-1. pp. 795-806. Workshop: Security, Datenschutz und Anonymisierung. Berlin. 27. September - 1. Oktober 2021
Zitierform
Tags