Immuno-mimetic Deep Neural Networks (Immuno-Net)

• URL: http://arxiv.org/abs/2107.02842v1
• Date: Sun, 27 Jun 2021 16:45:23 GMT
• Title: Immuno-mimetic Deep Neural Networks (Immuno-Net)
• Authors: Ren Wang, Tianqi Chen, Stephen Lindsly, Cooper Stansbury, Indika Rajapakse, Alfred Hero
• Abstract summary: We introduce a new type of biomimetic model, one that borrows concepts from the immune system. This immuno-mimetic model leads to a new computational biology framework for robustification of deep neural networks. We show that Immuno-net RAILS results in improvement of as much as 12.5% in adversarial accuracy of a baseline method.
• Score: 15.653578249331982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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