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Recent research has established that the local Lipschitz constant of a neuralnetwork directly influences its adversarial robustness. We exploit thisrelationship to construct an ensemble of neural networks which not onlyimproves the accuracy, but also provides increased adversarial robustness. Thelocal Lipschitz constants for two different ensemble methods - bagging andstacking - are derived and the architectures best suited for ensuringadversarial robustness are deduced. The proposed ensemble architectures aretested 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) asingle network and b) traditional ensemble methods.

The robust way to stack and bag: the local Lipschitz way