mlsanityData to 14 Oct 2026

[Re] Double Sampling Randomized Smoothing

arXiv 2306.15221 ↗First posted 27 Jun 2023cs.LG cs.CR math.STcited by 0

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Abstract (arXiv)

This paper is a contribution to the reproducibility challenge in the field of machine learning, specifically addressing the issue of certifying the robustness of neural networks (NNs) against adversarial perturbations. The proposed Double Sampling Randomized Smoothing (DSRS) framework overcomes the limitations of existing methods by using an additional smoothing distribution to improve the robustness certification. The paper provides a clear manifestation of DSRS for a generalized family of Gaussian smoothing and a computationally efficient method for implementation. The experiments on MNIST and CIFAR-10 demonstrate the effectiveness of DSRS, consistently certifying larger robust radii compared to other methods. Also various ablations studies are conducted to further analyze the hyperparameters and effect of adversarial training methods on the certified radius by the proposed framework.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 19 references that are in the map. First posted on arXiv 27 Jun 2023.

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17 of 19 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  3. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  4. 2017Provable defenses against adversarial examples via the convex outer adversarial polytopecited by 567
  5. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 468
  6. 2019Provably Robust Deep Learning via Adversarially Trained Smoothed Classifierscited by 330
  7. 2018Certified Adversarial Robustness with Additive Noisecited by 180
  8. 2020Randomized Smoothing of All Shapes and Sizescited by 147
  9. 2020MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radiuscited by 114
  10. 2020Consistency Regularization for Certified Robustness of Smoothed Classifierscited by 73
  11. 2020Curse of Dimensionality on Randomized Smoothing for Certifiable Robustnesscited by 56
  12. 2019Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracycited by 49
5 more
  1. 2021SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustnesscited by 49
  2. 2020Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Frameworkcited by 46
  3. 2020Random Smoothing Might be Unable to Certify ℓ_∞ Robustness for High-Dimensional Imagescited by 35
  4. 2022Double Sampling Randomized Smoothingcited by 22
  5. 2020Extensions and limitations of randomized smoothing for robustness guaranteescited by 15

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