  The security issues in DNNs, such as adversarial examples, have attracted
much attention. Adversarial examples refer to the examples which are capable to
induce the DNNs return completely predictions by introducing carefully designed
perturbations. Obviously, adversarial examples bring great security risks to
the development of deep learning. Recently, Some defense approaches against
adversarial examples have been proposed, however, in our opinion, the
performance of these approaches are still limited. In this paper, we propose a
new ensemble defense approach named the Negative Correlation Ensemble (NCEn),
which achieves compelling results by introducing gradient directions and
gradient magnitudes of each member in the ensemble negatively correlated and at
the same time, reducing the transferability of adversarial examples among them.
Extensive experiments have been conducted, and the results demonstrate that
NCEn can improve the adversarial robustness of ensembles effectively.
