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Sparse Coding Frontend for Robust Neural Networks

arXiv 2104.05353 ↗First posted 12 Apr 2021cs.LG stat.MLcited by 0

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Abstract (arXiv)

Deep Neural Networks are known to be vulnerable to small, adversarially crafted, perturbations. The current most effective defense methods against these adversarial attacks are variants of adversarial training. In this paper, we introduce a radically different defense trained only on clean images: a sparse coding based frontend which significantly attenuates adversarial attacks before they reach the classifier. We evaluate our defense on CIFAR-10 dataset under a wide range of attack types (including Linf , L2, and L1 bounded attacks), demonstrating its promise as a general-purpose approach for defense.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 20 references that are in the map. First posted on arXiv 12 Apr 2021.

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References · 17

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  2. 2013Intriguing properties of neural networkscited by 5,155
  3. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,517
  4. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  5. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,268
  6. 2017Evasion Attacks against Machine Learning at Test Timecited by 689
  7. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 666
  8. 2015Cyclical Learning Rates for Training Neural Networkscited by 629
  9. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 546
  10. 2017Countering Adversarial Images using Input Transformationscited by 520
  11. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 462
  12. 2019Unlabeled Data Improves Adversarial Robustnesscited by 416
5 more
  1. 2019HopSkipJumpAttack: A Query-Efficient Decision-Based Attackcited by 333
  2. 2017Parseval Networks: Improving Robustness to Adversarial Examplescited by 293
  3. 2017The Space of Transferable Adversarial Examplescited by 158
  4. 2020Torchattacks: A PyTorch Repository for Adversarial Attackscited by 158
  5. 2019ME-Net: Towards Effective Adversarial Robustness with Matrix Estimationcited by 65

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