Generative adversary network (GAN) generated high-realistic human faces have been used as profile images for fake social media accounts and are visually challenging to discern from real ones. In this work, we show that GAN-generated faces can be exposed via irregular pupil shapes. This phenomenon is caused by the lack of physiological constraints in the GAN models. We demonstrate that such artifacts exist widely in high-quality GAN-generated faces and further describe an automatic method to extract the pupils from two eyes and analysis their shapes for exposing the GAN-generated faces. Qualitative and quantitative evaluations of our method suggest its simplicity and effectiveness in distinguishing GAN-generated faces.

翻译：产生式对抗网络(GAN)生成了高现实的人类面孔,这些面孔被用作假社交媒体账户的图像,在视觉上很难辨别真实的面孔。在这项工作中,我们表明GAN产生的面孔可以通过不正常的学生形状暴露出来。这种现象是GAN模型缺乏生理限制造成的。我们证明这些手工艺品广泛存在于高质量的GAN产生的面孔中,并进一步描述了一种从两眼中提取学生的自动方法,并用分析其形状来暴露GAN产生的面孔。对我们的方法的定性和定量评估表明,在区分GAN产生的面孔方面,这种手工艺品是简单而有效的。