  Biomimetics has played a key role in the evolution of artificial neural
networks. Thus far, in silico metaphors have been dominated by concepts from
neuroscience and cognitive psychology. In this paper we introduce a different
type of biomimetic model, one that borrows concepts from the immune system, for
designing robust deep neural networks. This immuno-mimetic model leads to a new
computational biology framework for robustification of deep neural networks
against adversarial attacks. Within this Immuno-Net framework we define a
robust adaptive immune-inspired learning system (Immuno-Net RAILS) that
emulates, in silico, the adaptive biological mechanisms of B-cells that are
used to defend a mammalian host against pathogenic attacks. When applied to
image classification tasks on benchmark datasets, we demonstrate that
Immuno-net RAILS results in improvement of as much as 12.5% in adversarial
accuracy of a baseline method, the DkNN-robustified CNN, without appreciable
loss of accuracy on clean data.
