Sparse DNNs with Improved Adversarial Robustness

Authors: Yiwen Guo, Chao Zhang, Changshui Zhang, Yurong Chen
Published: 2018-10-23 | Updated: 2019-11-06

Source: https://arxiv.org/abs/1810.09619

PDF: https://arxiv.org/pdf/1810.09619

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データキュレーション ロバスト推定 スパース性の影響

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Abstract

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 l₂ 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.