• Corpus ID: 16716974

Adversarial Transformation Networks: Learning to Generate Adversarial Examples

@article{Baluja2017AdversarialTN,
  title={Adversarial Transformation Networks: Learning to Generate Adversarial Examples},
  author={Shumeet Baluja and Ian S. Fischer},
  journal={ArXiv},
  year={2017},
  volume={abs/1703.09387}
}
Multiple different approaches of generating adversarial examples have been proposed to attack deep neural networks. [] Key Method We call such a network an Adversarial Transformation Network (ATN). ATNs are trained to generate adversarial examples that minimally modify the classifier's outputs given the original input, while constraining the new classification to match an adversarial target class. We present methods to train ATNs and analyze their effectiveness targeting a variety of MNIST classifiers as…
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236 Citations
Highly Influential Citations
19
Background Citations
124
Methods Citations
60
Results Citations
4

236 Citations

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Generalizable Adversarial Attacks Using Generative Models

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Detecting Adversarial Examples Through Image Transformation

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Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples

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