2019/03/15 by Lakshmanan Nataraj, Tajuddin Manhar Mohammed, Nataraj, Lakshmanan +13 · 31 citations
Computer Science · Engineering · #Advanced Image Processing Techniques #Digital Media Forensic Detection #Generative Adversarial Networks and Image Synthesis #cs.CV #eess.IV
paper · pdf · doi:10.48550/arxiv.1903.06836
arxiv created 2019/10/03 · arxiv updated 2019/10/04
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.