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Conference Paper

Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction

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Sattar,  Hosnieh
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Pons-Moll,  Gerard
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Citation

Sattar, H., Krombholz, K., Pons-Moll, G., & Fritz, M. (2021). Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction. In A. Bartoli, & A. Fusiello (Eds.), Computer Vision -- ECCV Workshops 2020 (pp. 411-428). Berlin: Springer. doi:10.1007/978-3-030-68238-5_31.


Cite as: https://hdl.handle.net/21.11116/0000-0007-D755-7
Abstract
Modern approaches to pose and body shape estimation have recently achieved
strong performance even under challenging real-world conditions. Even from a
single image of a clothed person, a realistic looking body shape can be
inferred that captures a users' weight group and body shape type well. This
opens up a whole spectrum of applications -- in particular in fashion -- where
virtual try-on and recommendation systems can make use of these new and
automatized cues. However, a realistic depiction of the undressed body is
regarded highly private and therefore might not be consented by most people.
Hence, we ask if the automatic extraction of such information can be
effectively evaded. While adversarial perturbations have been shown to be
effective for manipulating the output of machine learning models -- in
particular, end-to-end deep learning approaches -- state of the art shape
estimation methods are composed of multiple stages. We perform the first
investigation of different strategies that can be used to effectively
manipulate the automatic shape estimation while preserving the overall
appearance of the original image.