  Although Generative Adversarial Network (GAN) can be used to generate the
realistic image, improper use of these technologies brings hidden concerns. For
example, GAN can be used to generate a tampered video for specific people and
inappropriate events, creating images that are detrimental to a particular
person, and may even affect that personal safety. In this paper, we will
develop a deep forgery discriminator (DeepFD) to efficiently and effectively
detect the computer-generated images. Directly learning a binary classifier is
relatively tricky since it is hard to find the common discriminative features
for judging the fake images generated from different GANs. To address this
shortcoming, we adopt contrastive loss in seeking the typical features of the
synthesized images generated by different GANs and follow by concatenating a
classifier to detect such computer-generated images. Experimental results
demonstrate that the proposed DeepFD successfully detected 94.7% fake images
generated by several state-of-the-art GANs.
