Detection, Attribution and Localization of GAN Generated Images
Abstract
Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or computers. GANs are used in a wide range of tasks, from modifying small attributes of an image (StarGAN [14]), transferring attributes between image pairs (CycleGAN [91]), as well as generating entirely new images (ProGAN [36], StyleGAN [37], SPADE/GauGAN [64]). In this paper, we propose a novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods. For every image, co-occurrence matrices are computed on neighborhood pixels of RGB channels in different directions (horizontal, vertical and diagonal). A deep learning network is then trained on these features to detect, attribute and localize these GAN generated/manipulated images. A large scale evaluation of our approach on 5 GAN datasets comprising over 2.76 million images (ProGAN, StarGAN, CycleGAN, StyleGAN and SPADE/GauGAN) shows promising results in detecting GAN generated images.
Keywords
Cite
@article{arxiv.2007.10466,
title = {Detection, Attribution and Localization of GAN Generated Images},
author = {Michael Goebel and Lakshmanan Nataraj and Tejaswi Nanjundaswamy and Tajuddin Manhar Mohammed and Shivkumar Chandrasekaran and B. S. Manjunath},
journal= {arXiv preprint arXiv:2007.10466},
year = {2020}
}