论文标题
GAN诱导的属性操纵是否会影响面部识别？
Can GAN-induced Attribute Manipulations Impact Face Recognition?
论文作者
论文摘要
由于人口，性，性别，种族等的影响，已经在自动化的面部识别系统中进行了广泛的研究。但是，\ textIt {数字修改}的人口统计学和面部属性对面部识别的影响相对较小。在这项工作中，我们研究了通过生成对抗网络（GAN）引起的属性操纵对面部识别性能的影响。我们通过使用ATTGAN和Stgan有意修改13个属性，并评估它们对两种基于深度学习的面部验证方法，Arcface和vggface的影响，在Celeba数据集上进行实验。我们的发现表明，涉及眼镜和性线索的数字变化的某些属性操纵可能会大大损害面部识别多达73％，需要进一步分析。
Impact due to demographic factors such as age, sex, race, etc., has been studied extensively in automated face recognition systems. However, the impact of \textit{digitally modified} demographic and facial attributes on face recognition is relatively under-explored. In this work, we study the effect of attribute manipulations induced via generative adversarial networks (GANs) on face recognition performance. We conduct experiments on the CelebA dataset by intentionally modifying thirteen attributes using AttGAN and STGAN and evaluating their impact on two deep learning-based face verification methods, ArcFace and VGGFace. Our findings indicate that some attribute manipulations involving eyeglasses and digital alteration of sex cues can significantly impair face recognition by up to 73% and need further analysis.
