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

Frick, Raphael AntoniusSteinebach, MartinGesellschaft für Informatik e.V. (GI)2021-12-142021-12-142021978-3-88579-708-1https://dl.gi.de/handle/20.500.12116/37735The 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 𝑘-same approach and utilizing different clustering techniques, privacy for the individuals involved can be greatly enhanced, while preserving important facial characteristics.enFacial De-Identificationk-AnonymityPrivacyGenerative Adversarial NetworksFacial Image DataAchieving Facial De-Identification by Taking Advantage of the Latent Space of Generative Adversarial Networks10.18420/informatik2021-0681617-5468