  Many works demonstrate that deep learning system is vulnerable to adversarial
attack. A deep learning system consists of two parts: the deep learning task
and the deep model. Nowadays, most existing works investigate the impact of the
deep model on robustness of deep learning systems, ignoring the impact of the
learning task. In this paper, we adopt the binary and interval label encoding
strategy to redefine the classification task and design corresponding loss to
improve robustness of the deep learning system. Our method can be viewed as
improving the robustness of deep learning systems from both the learning task
and deep model. Experimental results demonstrate that our learning task-aware
method is much more robust than traditional classification while retaining the
accuracy.
