  Deep neural networks (DNNs) are computationally/memory-intensive and
vulnerable to adversarial attacks, making them prohibitive in some real-world
applications. By converting dense models into sparse ones, pruning appears to
be a promising solution to reducing the computation/memory cost. This paper
studies classification models, especially DNN-based ones, to demonstrate that
there exists intrinsic relationships between their sparsity and adversarial
robustness. Our analyses reveal, both theoretically and empirically, that
nonlinear DNN-based classifiers behave differently under $l_2$ attacks from
some linear ones. We further demonstrate that an appropriately higher model
sparsity implies better robustness of nonlinear DNNs, whereas over-sparsified
models can be more difficult to resist adversarial examples.

Machine Learning;Cryptography and Security;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing;Machine Learning