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Detection, Attribution and Localization of GAN Generated Images
Michael Goebel,Lakshmanan Nataraj,Tejaswi Nanjundaswamy,Tajuddin Manhar Mohammed,Shivkumar Chandrasekaran,B.S. Manjunath +5 more
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TLDR
A novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods is proposed.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.read more
Citations
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Robust Attentive Deep Neural Network for Detecting GAN-Generated Faces
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TL;DR: In this article , a Multi-Colorspace fused EfficientNet model is proposed to distinguish natural images from photo-realistic computer-generated images as a three-class classification task classifying natural, computer graphics and GAN images.
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