An Experimental Consideration on Gait Spoofing
An Experimental Consideration on Gait Spoofing
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DOI：
10.5220/0011661200003417
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Yuki Hirose;Kazuaki Nakamura;Naoko Nitta;N. Babaguchi
中科院分区：
文献类型：
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作者：
Yuki Hirose;Kazuaki Nakamura;Naoko Nitta;N. Babaguchi
：深度学习技术提高了生物识别系统的性能，并增加了针对它们的Spoofing攻击的风险。到目前为止，已经提出了许多针对人脸和语音的SPOOfing和反Spoofing方法。然而，对于步态来说，专注于Spofing风险的研究数量有限。为了检验步态Spoofing的可执行性，本文尝试从目标人的单张照片中生成一系列模拟目标人行走方式的假步态轮廓。从这样一张照片中提取的特征向量不具有关于目标人的步态特征的全部信息。为了补充信息，我们更新提取的特征，使其同时包含各种人的特征，就像狼样本一样。受Wolf样本或“主”样本的启发，我们将所提出的过程称为“主化”，它可以像万能钥匙一样同时通过两个或多个Verifi阳离子系统。在精化后，我们将其合成的特征向量解码为步态轮廓序列。在我们的实验中，生成的伪轮廓序列的步态识别准确率通过掌握从69%提高到78%，这表明步态Spoofing的风险是不可忽视的。
: Deep learning technologies have improved the performance of biometric systems as well as increased the risk of spoofing attacks against them. So far, lots of spoofing and anti-spoofing methods were proposed for face and voice. However, for gait, there are a limited number of studies focusing on the spoofing risk. To examine the executability of gait spoofing, in this paper, we attempt to generate a sequence of fake gait silhouettes that mimics a certain target person’s walking style only from his/her single photo. A feature vector extracted from such a single photo does not have full information about the target person’s gait characteristics. To complement the information, we update the extracted feature so that it simultaneously contains various people’s characteristics like a wolf sample. Inspired by a wolf sample or also called “master” sample, which can simultaneously pass two or more verification systems like a master key, we call the proposed process “masterization”. After the masterization, we decode its resultant feature vector to a gait silhouette sequence. In our experiment, the gait recognition accuracy with the generated fake silhouette sequences is increased from 69% to 78% by the masterization, which indicates an unignorable risk of gait spoofing.