Using a target model's latent features as the conditioning input to a GAN generator yields higher adversarial attack success rates on MNIST and CIFAR-10 than AdvGAN's image-conditioned generator.
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AdvGAN++ : Harnessing latent layers for adversary generation
Using a target model's latent features as the conditioning input to a GAN generator yields higher adversarial attack success rates on MNIST and CIFAR-10 than AdvGAN's image-conditioned generator.