The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks.

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
ACM Transactions on Applied Perception Pub Date : 2024-01-01 Epub Date: 2023-12-09 DOI:10.1145/3618113
Snipta Mallick, Géraldine Jeckeln, Connor J Parde, Carlos D Castillo, Alice J O'Toole
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引用次数: 0

Abstract

Facial morphs created between two identities resemble both of the faces used to create the morph. Consequently, humans and machines are prone to mistake morphs made from two identities for either of the faces used to create the morph. This vulnerability has been exploited in "morph attacks" in security scenarios. Here, we asked whether the "other-race effect" (ORE)-the human advantage for identifying own- vs. other-race faces-exacerbates morph attack susceptibility for humans. We also asked whether face-identification performance in a deep convolutional neural network (DCNN) is affected by the race of morphed faces. Caucasian (CA) and East-Asian (EA) participants performed a face-identity matching task on pairs of CA and EA face images in two conditions. In the morph condition, different-identity pairs consisted of an image of identity "A" and a 50/50 morph between images of identity "A" and "B". In the baseline condition, morphs of different identities never appeared. As expected, morphs were identified mistakenly more often than original face images. Of primary interest, morph identification was substantially worse for cross-race faces than for own-race faces. Similar to humans, the DCNN performed more accurately for original face images than for morphed image pairs. Notably, the deep network proved substantially more accurate than humans in both cases. The results point to the possibility that DCNNs might be useful for improving face identification accuracy when morphed faces are presented. They also indicate the significance of the race of a face in morph attack susceptibility in applied settings.

其他种族效应对人脸变形攻击易感性的影响
在两个身份之间创建的面部变形与用于创建变形的两张脸相似。因此,人类和机器很容易将两种身份的变体误认为是用于创建变体的任何一张脸。安全场景中的“变形攻击”已经利用了这个漏洞。在这里,我们询问“其他种族效应”(ORE)——人类识别自己与其他种族面孔的优势——是否加剧了人类的变形攻击易感性。我们还询问了深度卷积神经网络(DCNN)中的人脸识别性能是否受到变形人脸种族的影响。高加索人(CA)和东亚人(EA)在两种情况下对高加索人和东亚人的面部图像进行了面部身份匹配任务。在变形条件下,不同同一性对由身份“A”的图像和身份“A”与身份“B”的图像之间50/50的变形组成。在基线条件下,不同身份的变体从未出现。不出所料,变体比原始人脸更容易被错误识别。最重要的是,与同种族的面孔相比,跨种族面孔的形态识别要差得多。与人类相似,DCNN对原始人脸图像的识别比对变形图像的识别更准确。值得注意的是,深度网络在这两种情况下都比人类准确得多。结果表明,当呈现变形的人脸时,DCNNs可能有助于提高人脸识别的准确性。它们还表明了在应用环境中面部种族对变形攻击敏感性的重要性。
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来源期刊
ACM Transactions on Applied Perception
ACM Transactions on Applied Perception 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
22
审稿时长
12 months
期刊介绍: ACM Transactions on Applied Perception (TAP) aims to strengthen the synergy between computer science and psychology/perception by publishing top quality papers that help to unify research in these fields. The journal publishes inter-disciplinary research of significant and lasting value in any topic area that spans both Computer Science and Perceptual Psychology. All papers must incorporate both perceptual and computer science components.
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