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Deep Adversarially-Enhanced k-Nearest Neighbors

arXiv 2108.06797 ↗First posted 15 Aug 2021cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Recent works have theoretically and empirically shown that deep neural networks (DNNs) have an inherent vulnerability to small perturbations. Applying the Deep k-Nearest Neighbors (DkNN) classifier, we observe a dramatically increasing robustness-accuracy trade-off as the layer goes deeper. In this work, we propose a Deep Adversarially-Enhanced k-Nearest Neighbors (DAEkNN) method which achieves higher robustness than DkNN and mitigates the robustness-accuracy trade-off in deep layers through two key elements. First, DAEkNN is based on an adversarially trained model. Second, DAEkNN makes predictions by leveraging a weighted combination of benign and adversarial training data. Empirically, we find that DAEkNN improves both the robustness and the robustness-accuracy trade-off on MNIST and CIFAR-10 datasets.

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References · 20

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
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8 more
  1. 2019Deep Learning for Hyperspectral Image Classification: An Overviewcited by 121
  2. 2019Deep Learning vs. Traditional Computer Visioncited by 94
  3. 2020Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Casescited by 93
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  6. 2021On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learningcited by 29
  7. 2019Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Searchcited by 19
  8. 2021RAILS: A Robust Adversarial Immune-inspired Learning Systemcited by 8

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