Corpus ID: 208175576

Adversarial Robustness of Flow-Based Generative Models

@article{Pope2020AdversarialRO,
  title={Adversarial Robustness of Flow-Based Generative Models},
  author={P. Pope and Y. Balaji and S. Feizi},
  journal={ArXiv},
  year={2020},
  volume={abs/1911.08654}
}

P. Pope, Y. Balaji, S. Feizi

Published 2020

Computer Science, Mathematics

ArXiv

Flow-based generative models leverage invertible generator functions to fit a distribution to the training data using maximum likelihood. Despite their use in several application domains, robustness of these models to adversarial attacks has hardly been explored. In this paper, we study adversarial robustness of flow-based generative models both theoretically (for some simple models) and empirically (for more complex ones). First, we consider a linear flow-based generative model and compute… CONTINUE READING

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