Exposing GAN-synthesized Faces Using Landmark Locations
Computer Science Department, University at Albany, State University of New York（纽约州立大学奥尔巴尼分校计算机科学系）；School of Computer and Control Engineering, University of the Chinese Academy of Sciences（中国科学院大学计算机与控制工程学院）
英文摘要：
Generative adversary networks (GANs) have recently led to highly realistic image synthesis results. In this work, we describe a new method to expose GAN-synthesized images using the locations of the facial landmark points. Our method is based on the observations that the facial parts configuration generated by GAN models are different from those of the real faces, due to the lack of global constraints. We perform experiments demonstrating this phenomenon, and show that an SVM classifier trained using the locations of facial landmark points is sufficient to achieve good classification performance for GAN-synthesized faces.
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