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Robust Deep Learning Ensemble against Deception
[article]
2020
arXiv
pre-print
Deep neural network (DNN) models are known to be vulnerable to maliciously crafted adversarial examples and to out-of-distribution inputs drawn sufficiently far away from the training data. How to protect a machine learning model against deception of both types of destructive inputs remains an open challenge. This paper presents XEnsemble, a diversity ensemble verification methodology for enhancing the adversarial robustness of DNN models against deception caused by either adversarial examples
arXiv:2009.06589v1
fatcat:j4j72zudnbf5phqlp4wfkporee