First Learning Steps to Recognize Faces in the Noise

被引：0

作者：

Lamminger, Lukas ^{[1
]}

Hofbauer, Heinz ^{[1
]}

Uhl, Andreas ^{[1
]}

机构：

[1] Univ Salzburg, Dept Artificial Intelligence & Human Interfaces, Salzburg, Austria

来源：

PROCEEDINGS OF THE 2023 ACM WORKSHOP ON INFORMATION HIDING AND MULTIMEDIA SECURITY, IH&MMSEC 2023 | 2023年

基金：

奥地利科学基金会;

关键词：

selective encryption; face recognition; denoising; inpaiting; deep learning;

D O I：

10.1145/3577163.3595105

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：139 / 144

页数：6

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