Adversarial defenses via a mixture of generators
- URL: http://arxiv.org/abs/2110.02364v1
- Date: Tue, 5 Oct 2021 21:27:50 GMT
- Title: Adversarial defenses via a mixture of generators
- Authors: Maciej \.Zelaszczyk and Jacek Ma\'ndziuk
- Abstract summary: adversarial examples remain a relatively weakly understood feature of deep learning systems.
We show that it is possible to train such a system without supervision, simultaneously on multiple adversarial attacks.
Our system is able to recover class information for previously-unseen examples with neither attack nor data labels on the MNIST dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In spite of the enormous success of neural networks, adversarial examples
remain a relatively weakly understood feature of deep learning systems. There
is a considerable effort in both building more powerful adversarial attacks and
designing methods to counter the effects of adversarial examples. We propose a
method to transform the adversarial input data through a mixture of generators
in order to recover the correct class obfuscated by the adversarial attack. A
canonical set of images is used to generate adversarial examples through
potentially multiple attacks. Such transformed images are processed by a set of
generators, which are trained adversarially as a whole to compete in inverting
the initial transformations. To our knowledge, this is the first use of a
mixture-based adversarially trained system as a defense mechanism. We show that
it is possible to train such a system without supervision, simultaneously on
multiple adversarial attacks. Our system is able to recover class information
for previously-unseen examples with neither attack nor data labels on the MNIST
dataset. The results demonstrate that this multi-attack approach is competitive
with adversarial defenses tested in single-attack settings.
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