  Recently the GAN generated face images are more and more realistic with
high-quality, even hard for human eyes to detect. On the other hand, the
forensics community keeps on developing methods to detect these generated fake
images and try to guarantee the credibility of visual contents. Although
researchers have developed some methods to detect generated images, few of them
explore the important problem of generalization ability of forensics model. As
new types of GANs are emerging fast, the generalization ability of forensics
models to detect new types of GAN images is absolutely an essential research
topic. In this paper, we explore this problem and propose to use preprocessed
images to train a forensic CNN model. By applying similar image level
preprocessing to both real and fake training images, the forensics model is
forced to learn more intrinsic features to classify the generated and real face
images. Our experimental results also prove the effectiveness of the proposed
method.
