论文标题
使用不一致的角膜镜头亮点暴露gan生成的面孔
Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights
论文作者
论文摘要
复杂的生成对手网络（GAN）模型现在能够合成高度现实的人面孔，这些人面孔很难从视觉上辨别出真实的面孔。在这项工作中，我们表明gan合成的面孔可以通过两只眼睛之间的不一致的角膜镜头亮点暴露。不一致是由于GAN模型中缺乏物理/生理约束而引起的。我们表明，这种伪影在高质量的gan合成面中广泛存在，并进一步描述了一种自动方法，以提取和比较两只眼睛的角膜镜头亮点。对我们方法的定性和定量评估表明，它在区分GAN合成面时的简单性和有效性。
Sophisticated generative adversary network (GAN) models are now able to synthesize highly realistic human faces that are difficult to discern from real ones visually. In this work, we show that GAN synthesized faces can be exposed with the inconsistent corneal specular highlights between two eyes. The inconsistency is caused by the lack of physical/physiological constraints in the GAN models. We show that such artifacts exist widely in high-quality GAN synthesized faces and further describe an automatic method to extract and compare corneal specular highlights from two eyes. Qualitative and quantitative evaluations of our method suggest its simplicity and effectiveness in distinguishing GAN synthesized faces.
