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Towards adversarial robustness with 01 loss neural networks
[article]
2020
arXiv
pre-print
Motivated by the general robustness properties of the 01 loss we propose a single hidden layer 01 loss neural network trained with stochastic coordinate descent as a defense against adversarial attacks in machine learning. One measure of a model's robustness is the minimum distortion required to make the input adversarial. This can be approximated with the Boundary Attack (Brendel et. al. 2018) and HopSkipJump (Chen et. al. 2019) methods. We compare the minimum distortion of the 01 loss network
arXiv:2008.09148v1
fatcat:lkvy5tztazco7djd7r2rnwcdgq