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Computer Science > Computer Vision and Pattern Recognition

arXiv:2106.02328 (cs)
[Submitted on 4 Jun 2021]

Title:Temporally coherent video anonymization through GAN inpainting

Authors:Thangapavithraa Balaji, Patrick Blies, Georg Göri, Raphael Mitsch, Marcel Wasserer, Torsten Schön
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Abstract:This work tackles the problem of temporally coherent face anonymization in natural video this http URL propose JaGAN, a two-stage system starting with detecting and masking out faces with black image patches in all individual frames of the video. The second stage leverages a privacy-preserving Video Generative Adversarial Network designed to inpaint the missing image patches with artificially generated faces. Our initial experiments reveal that image based generative models are not capable of inpainting patches showing temporal coherent appearance across neighboring video frames. To address this issue we introduce a newly curated video collection, which is made publicly available for the research community along with this paper. We also introduce the Identity Invariance Score IdI as a means to quantify temporal coherency between neighboring frames.
Comments: Preprint of our FG2021 submission
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2106.02328 [cs.CV]
  (or arXiv:2106.02328v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2106.02328

Submission history

From: Torsten Schön [view email]
[v1] Fri, 4 Jun 2021 08:19:44 UTC (13,484 KB)
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