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Attributing and Detecting Fake Images Generated by Known GANs
2020
2020 IEEE Security and Privacy Workshops (SPW)
The quality of GAN-generated fake images has improved significantly, and recent GAN approaches, such as StyleGAN, achieve near indistinguishability from real images for the naked eye. As a result, adversaries are attracted to using GAN-generated fake images for disinformation campaigns and fraud on social networks. However, training an image generation network to produce realistic-looking samples remains a timeconsuming and difficult problem, so adversaries are more likely to use published GAN
doi:10.1109/spw50608.2020.00019
fatcat:5byl2rzdl5a7xfwmdngbgdd2l4