Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning

04/24/2018

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by   Arezoo Rajabi, et al.

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Detection and rejection of adversarial examples in security sensitive and safety-critical systems using deep CNNs is essential. In this paper, we propose an approach to augment CNNs with out-distribution learning in order to reduce misclas- sification rate by rejecting adversarial examples. We empirically show that our augmented CNNs can either reject or classify correctly most adversarial examples generated using well-known methods ( >95 >75 to train using any specific type of adversarial examples and without sacrificing the accuracy of models on clean samples significantly (< 4