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The robust way to stack and bag: the local Lipschitz way
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Recent research has established that the local Lipschitz constant of a neural network directly influences its adversarial robustness. We exploit this relationship to construct an ensemble of neural networks which not only improves the accuracy, but also provides increased adversarial robustness. The local Lipschitz constants for two different ensemble methods - bagging and stacking - are derived and the architectures best suited for ensuring adversarial robustness are deduced. The proposed ensemble architectures are tested on MNIST and CIFAR-10 datasets in the presence of white-box attacks, FGSM and PGD. The proposed architecture is found to be more robust than a) a single network and b) traditional ensemble methods.
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Cited by 1 Pith paper
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TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification
TAROT derives a new robust margin disparity generalization bound and uses it to train domain-invariant, adversarially robust classifiers that outperform prior robust UDA methods on standard benchmarks.
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