Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2018

Abstract

As more and more personal photos are shared online, being able to obfuscate identities in such photos is becoming a necessity for privacy protection. People have largely resorted to blacking out or blurring head regions, but they result in poor user experience while being surprisingly ineffective against state of the art person recognizers. In this work, we propose a novel head inpainting obfuscation technique. Generating a realistic head inpainting in social media photos is challenging because subjects appear in diverse activities and head orientations. We thus split the task into two sub-tasks: (1) facial landmark generation from image context (e.g. body pose) for seamless hypothesis of sensible head pose, and (2) facial landmark conditioned head inpainting. We verify that our inpainting method generates realistic person images, while achieving superior obfuscation performance against automatic person recognizers.

Keywords

Person image generation, identity obfuscation, privacy protection, generative adversarial networks

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): Salt Lake City, UT, June 18-22: Proceedings

First Page

5050

Last Page

5059

ISBN

9781538664209

Identifier

10.1109/CVPR.2018.00530

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/CVPR.2018.00530

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