An Adversarial Robustness Perspective on the Topology of Neural Networks. (arXiv:2211.02675v1 [cs.LG])

In this paper, we investigate the impact of neural networks (NNs) topology on
adversarial robustness. Specifically, we study the graph produced when an input
traverses all the layers of a NN, and show that such graphs are different for
clean and adversarial inputs. We find that graphs from clean inputs are more
centralized around highway edges, whereas those from adversaries are more
diffuse, leveraging under-optimized edges. Through experiments on a variety of
datasets and architectures, we show that these under-optimized …

adversarial networks neural networks robustness