First Learning Steps to Recognize Faces in the Noise
Pages 139 - 144
Abstract
A UNet-type encoder-decoder inpainting network is applied to weaken the protection strength of selectively encrypted face samples. Based on visual assessment, FaceQNet quality, and ArcFace recognition accuracy the strategy is shown to be successful, however, to a different extent depending on the original protection strength. For almost cryptographic strength, inpainting does not cause a practically relevant protection weakening, while for lower original protection strength inpainting almost removes the protection entirely.
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Index Terms
- First Learning Steps to Recognize Faces in the Noise
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Published In
June 2023
190 pages
ISBN:9798400700545
DOI:10.1145/3577163
- General Chair:
- Daniel Moreira,
- Program Chairs:
- Aparna Bharati,
- Cecilia Pasquini,
- Yassine Yousfi
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.
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Association for Computing Machinery
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Published: 28 June 2023
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- Austrian Science Fund (FWF)
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IH&MMSec '23
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IH&MMSec '23: ACM Workshop on Information Hiding and Multimedia Security
June 28 - 30, 2023
IL, Chicago, USA
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Overall Acceptance Rate 128 of 318 submissions, 40%
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