Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning
Year: 2018
Type: preprint
Abstract: 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%).
Source: arXiv (Cornell University)
Institutions: Université Laval, Oregon State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Nvidia, Compute Canada, Mitacs
OpenAlex ID: W4302294897