AdvGAN++ : Harnessing latent layers for adversary generation

Mangla, Puneet and Balasubramanian, Vineeth N and Varshney, Sakshi and et al, . (2019) AdvGAN++ : Harnessing latent layers for adversary generation.


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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 same time generates perceptually realistic images on MNIST and CIFAR10 datasets.

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IITH Creators:
IITH CreatorsORCiD
Balasubramanian, Vineeth NUNSPECIFIED
Item Type: Article
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 19 Aug 2019 09:02
Last Modified: 19 Aug 2019 09:02
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