其他种族影响对面对形态化攻击的易感性的影响
Snipta Mallick1, Géraldine Jeckeln1, Connor J Parde1
1School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX.
ACM transactions on applied perception
|August 12, 2024
概括
其他种族效应 (ORE) 增加了面部形态攻击的易感性. 深层卷积神经网络 (DCNNs) 在识别形态化面部方面表现出比人类更高的准确性,无论种族如何.
科学领域:
- 认知心理学 认知心理学
- 计算机视觉 计算机视觉
- 生物识别信息 生物识别信息
背景情况:
- 面部变形可以欺骗人类和机器,通过变形攻击构成安全风险.
- 其他种族效应 (ORE) 证明了人类在识别自己的种族与其他种族面孔方面的偏见.
- 深度卷积神经网络 (DCNNs) 越来越多地用于面部识别,但它们在操纵图像上的表现却不太了解.
研究的目的:
- 调查其他种族效应 (ORE) 是否加剧了人类对面部形态变异攻击的易感性.
- 确定变形面孔的种族是否会影响面部识别中的深卷积神经网络 (DCNNs) 的性能.
- 为了比较人类和DCNN在识别原始与变形面孔方面的表现,考虑种族因素.
主要方法:
- 参与者 (高加索和东亚) 执行了与原始和变形的面部图像相匹配的面部身份匹配任务.
- 使用了两个条件:一个具有50/50身份形态的形态条件和一个没有形态的基线条件.
- 在相同的面部识别任务中,还测试了深度卷积神经网络 (DCNN).
主要成果:
- 形态表象比原始面孔更常被误认,这证实了它们的欺骗潜力.
- 面部形态识别的准确性在人类的种族相比,与自己种族相比,跨种族的面部显著较低.
- 不管种族,DCNN在识别原始和变形的脸上都比人类更准确.
结论:
- 其他种族效应 (ORE) 显著增加了人类面部形态攻击的脆弱性.
- 深层卷积神经网络 (DCNNs) 显示出增强面部识别准确性的前景,特别是在检测变形图像方面.
- 面部的种族是影响真实世界安全应用中易受变形攻击的关键因素.
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