Explaining Adversarial Examples by Local Properties of Convolutional Neural Networks

Hamed H. Aghdam, Elnaz J. Heravi, Domenec Puig

Abstract

Vulnerability of ConvNets to adversarial examples have been mainly studied by devising a solution for generating adversarial examples. Early studies suggested that sensitivity of ConvNets to adversarial examples are due to their non-linearity. Most recent studies explained that instability of ConvNet to these examples are because of their linear nature. In this work, we analyze some of local properties of ConvNets that are directly related to their unreliability to adversarial examples. We shows that ConvNets are not locally isotropic and symmetric. Also, we show that Mantel score of distance matrices in the input and output of a ConvNet is very low showing that topology of points located at a very close distance to a samples might significantly change by ConvNets. We also explain that non-linearity of topology changes in ConvNet are because they apply an affine transformation in each layer. Furthermore, we explain that despite the fact that global Lipschitz constant of a ConvNet might be greater than 1, it is locally less than 1 in most of adversarial examples.

References

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Paper Citation


in Harvard Style

Aghdam H., Heravi E. and Puig D. (2017). Explaining Adversarial Examples by Local Properties of Convolutional Neural Networks . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 226-234. DOI: 10.5220/0006123702260234


in Bibtex Style

@conference{visapp17,
author={Hamed H. Aghdam and Elnaz J. Heravi and Domenec Puig},
title={Explaining Adversarial Examples by Local Properties of Convolutional Neural Networks},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={226-234},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006123702260234},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Explaining Adversarial Examples by Local Properties of Convolutional Neural Networks
SN - 978-989-758-226-4
AU - Aghdam H.
AU - Heravi E.
AU - Puig D.
PY - 2017
SP - 226
EP - 234
DO - 10.5220/0006123702260234