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Improving Adversarial Robustness by Enforcing Local and Global Compactness

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Computer Vision – ECCV 2020 (ECCV 2020)

Abstract

The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application. Among many developed defense models against such attacks, adversarial training emerges as the most successful method that consistently resists a wide range of attacks. In this work, based on an observation from a previous study that the representations of a clean data example and its adversarial examples become more divergent in higher layers of a deep neural net, we propose the Adversary Divergence Reduction Network which enforces local/global compactness and the clustering assumption over an intermediate layer of a deep neural network. We conduct comprehensive experiments to understand the isolating behavior of each component (i.e., local/global compactness and the clustering assumption) and compare our proposed model with state-of-the-art adversarial training methods. The experimental results demonstrate that augmenting adversarial training with our proposed components can further improve the robustness of the network, leading to higher unperturbed and adversarial predictive performances.

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Notes

  1. 1.

    The performance of TRADES is influenced by the model architectures and parameter tunings. The works  [10, 20] also reported that TRADES cannot surpass ADV all the time which explains the lower performance of TRADES on ResNet architecture in this paper. More analysis can be found in the supplementary material.

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Acknowledgement

This work was partially supported by the Australian Defence Science and Technology (DST) Group under the Next Generation Technology Fund (NTGF) scheme.

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Correspondence to Anh Bui .

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Bui, A. et al. (2020). Improving Adversarial Robustness by Enforcing Local and Global Compactness. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12372. Springer, Cham. https://doi.org/10.1007/978-3-030-58583-9_13

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  • DOI: https://doi.org/10.1007/978-3-030-58583-9_13

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  • Online ISBN: 978-3-030-58583-9

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