Detecting GAN generated Fake Images using Co-occurrence Matrices
Abstract
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.
Cite
@article{arxiv.1903.06836,
title = {Detecting GAN generated Fake Images using Co-occurrence Matrices},
author = {Lakshmanan Nataraj and Tajuddin Manhar Mohammed and Shivkumar Chandrasekaran and Arjuna Flenner and Jawadul H. Bappy and Amit K. Roy-Chowdhury and B. S. Manjunath},
journal= {arXiv preprint arXiv:1903.06836},
year = {2019}
}