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arXiv · 1908.00706

AdvGAN++ : Harnessing latent layers for adversary generation

Puneet Mangla·Surgan Jandial·Sakshi Varshney·Vineeth N Balasubramanian
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Abstract

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.

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Puneet Mangla, Surgan Jandial, Sakshi Varshney, Vineeth N Balasubramanian. 2019-08-02. AdvGAN++ : Harnessing latent layers for adversary generation. https://arxiv.org/abs/1908.00706

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