IAM

RESEARCH

Adversarial Robustness and Generalization

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Abstract

Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis [][] even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. In an effort to clarify the relationship between robustness and generalization, we assume an underlying, low-dimensional data manifold and show that: 1. regular adversarial examples leave the manifold; 2. adversarial examples constrained to the manifold, i.e., on-manifold adversarial examples, exist; 3. on-manifold adversarial examples are generalization errors, and on-manifold adversarial training boosts generalization; 4. and regular robustness is independent of generalization. These assumptions imply that both robust and accurate models are possible. However, different models (architectures, training strategies etc.) can exhibit different robustness and generalization characteristics. To confirm our claims, we present extensive experiments on synthetic data (with access to the true manifold) as well as on EMNIST [], Fashion-MNIST [] and CelebA [].

Download & Citing

The paper is available as pre-print on ArXiv: Paper on ArXiv
@article{Stutz2018ARXIV,
    author    = {David Stutz and Matthias Hein and Bernt Schiele},
    title     = {Disentangling Adversarial Robustness and Generalization},
    journal   = {CoRR},
    volume    = {abs/1812.00740},
    year      = {2018},
}

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References

  • [] D. Su, H. Zhang, H. Chen, J. Yi, P.-Y. Chen, and Y. Gao. Is robustness the cost of accuracy? – a comprehensive study on the robustness of 18 deep image classification models. arXiv.org, abs/1808.01688, 2018.
  • [] D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry. Robustness may be at odds with accuracy. arXiv.org, abs/1805.12152, 2018.
  • [] G. Cohen, S. Afshar, J. Tapson, and A. van Schaik. EMNIST: an extension of MNIST to handwritten letters. arXiv.org, abs/1702.05373, 2017.
  • [] H. Xiao, K. Rasul, and R. Vollgraf. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv.org, abs/1708.07747, 2017.
  • [] Z. Liu, P. Luo, X. Wang, and X. Tang. Deep learning face attributes in the wild. In ICCV, 2015.