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

Understand

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 misclassification 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% for MNIST and >75% for CIFAR-10 on average).
• Furthermore, we achieve this without requiring to train using any specific type of adversarial examples and without sacrificing the accuracy of models on clean samples significantly (< 4%).