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Face Swapping for Solving Collateral Privacy Issues in Multimedia Analytics

  • 2019
  • OriginalPaper
  • Chapter
Published in:
Author
Werner Bailer
Published in
MultiMedia Modeling
Publisher
Springer International Publishing

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Abstract

A wide range of components of multimedia analytics systems relies on visual content that is used for supervised (e.g., classification) and unsupervised (e.g., clustering) machine learning methods. This content may contain privacy sensitive information, e.g., show faces of persons. In many cases it is just an inevitable side-effect that persons appear in the content, and the application may not require identification – a situation which we call “collateral privacy issues”. We propose de-identification of faces in images by using a generative adversarial network to generate new face images, and use them to replace faces in the original images. We demonstrate that face swapping does not impact the performance of visual descriptor matching and extraction.

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previous chapter Integration of Exploration and Search: A Case Study of the M Model
next chapter Exploring the Impact of Training Data Bias on Automatic Generation of Video Captions
Title
Face Swapping for Solving Collateral Privacy Issues in Multimedia Analytics
Author
Werner Bailer
Copyright Year
2019
Book
MultiMedia Modeling

Print ISBN: 978-3-030-05709-1

Electronic ISBN: 978-3-030-05710-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-05710-7_14
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