AdvGAN++ : Harnessing latent layers for adversary generation
IIT Hyderabad（印度理工学院海得拉巴分校）
英文摘要：
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 same time generates perceptually realistic images on MNIST and CIFAR-10 datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
