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Detection, Attribution and Localization of GAN Generated Images
[article]
2020
arXiv
pre-print
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,
arXiv:2007.10466v1
fatcat:dt3uho2im5exhcvhfy2qdasrxy