  The advent of Generative Adversarial Networks (GANs) has brought about
completely novel ways of transforming and manipulating pixels in digital
images. GAN based techniques such as Image-to-Image translations, DeepFakes,
and other automated methods have become increasingly popular in creating fake
images. In this paper, we propose a novel approach to detect GAN generated fake
images using a combination of co-occurrence matrices and deep learning. We
extract co-occurrence matrices on three color channels in the pixel domain and
train a model using a deep convolutional neural network (CNN) framework.
Experimental results on two diverse and challenging GAN datasets comprising
more than 56,000 images based on unpaired image-to-image translations (cycleGAN
[1]) and facial attributes/expressions (StarGAN [2]) show that our approach is
promising and achieves more than 99% classification accuracy in both datasets.
Further, our approach also generalizes well and achieves good results when
trained on one dataset and tested on the other.
