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Adversarial examples are fabricated examples, indistinguishable from the original image that mislead neural networks and drastically lower their performance. Recently proposed AdvGAN, a GAN based approach, takes input image as a prior for generating adversaries to target a model. In this work, we show how latent features can serve as better priors than input images for adversary generation by proposing AdvGAN++, a version of AdvGAN that achieves higher attack rates than AdvGAN and at the samedoi:10.1109/iccvw.2019.00257 dblp:conf/iccvw/JandialMVB19 fatcat:c4advuefhbbtzp5t4jfeieaouy