Dual Randomized Smoothing: Beyond Global Noise Variance

• URL: http://arxiv.org/abs/2512.01782v1
• Date: Mon, 01 Dec 2025 15:23:00 GMT
• Title: Dual Randomized Smoothing: Beyond Global Noise Variance
• Authors: Chenhao Sun, Yuhao Mao, Martin Vechev,
• Abstract summary: Randomized Smoothing (RS) is a technique for certifying the robustness of neural networks against adversarial perturbations.<n>With RS, achieving high accuracy at small radii requires a small noise variance, while achieving high accuracy at large radii requires a large noise variance.<n>We propose a dual RS framework which enables input-dependent noise variances.
• Score: 4.921570751023892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Randomized Smoothing (RS) is a prominent technique for certifying the robustness of neural networks against adversarial perturbations. With RS, achieving high accuracy at small radii requires a small noise variance, while achieving high accuracy at large radii requires a large noise variance. However, the global noise variance used in the standard RS formulation leads to a fundamental limitation: there exists no global noise variance that simultaneously achieves strong performance at both small and large radii. To break through the global variance limitation, we propose a dual RS framework which enables input-dependent noise variances. To achieve that, we first prove that RS remains valid with input-dependent noise variances, provided the variance is locally constant around each input. Building on this result, we introduce two components which form our dual RS framework: (i) a variance estimator first predicts an optimal noise variance for each input, (ii) this estimated variance is then used by a standard RS classifier. The variance estimator is independently smoothed via RS to ensure local constancy, enabling flexible design. We also introduce training strategies to iteratively optimize the two components. Extensive experiments on CIFAR-10 show that our dual RS method provides strong performance for both small and large radii-unattainable with global noise variance-while incurring only a 60% computational overhead at inference. Moreover, it consistently outperforms prior input-dependent noise approaches across most radii, with particularly large gains at radii 0.5, 0.75, and 1.0, achieving relative improvements of 19%, 24%, and 21%, respectively. On ImageNet, dual RS remains effective across all radii. Additionally, the dual RS framework naturally provides a routing perspective for certified robustness, improving the accuracy-robustness trade-off with off-the-shelf expert RS models.

Related papers

• Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classification [55.56234913868664]
  We propose Test-time Adaptive Hierarchical Co-enhanced Denoising Network (TAHCD) for reliable learning on multimodal data.<n>The proposed method achieves superior classification performance, robustness, and generalization compared with state-of-the-art reliable multimodal learning approaches.
  arXiv  Detail & Related papers  (2026-01-12T03:14:12Z)
• From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals [20.323837731778358]
  Implicit feedback is central to recommender systems but is inherently noisy, often impairing model training and degrading user experience.<n>We propose textbfEARD (textbfEntity-textbfAware textbfReliability-textbfDriven Denoising), a lightweight framework that shifts the focus from interaction-level signals to entity-level reliability.
  arXiv  Detail & Related papers  (2025-08-14T17:20:12Z)
• Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation [91.83820250747935]
  Pseudo-label noise is mainly contained in unstable samples in which predictions of most pixels undergo significant variations during self-training. We introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples. SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings.
  arXiv  Detail & Related papers  (2024-06-10T21:44:52Z)
• Estimating the Robustness Radius for Randomized Smoothing with 100$\times$ Sample Efficiency [6.199300239433395]
  This work demonstrates that reducing the number of samples by one or two orders of magnitude can still enable the computation of a slightly smaller robustness radius. We provide the mathematical foundation for explaining the phenomenon while experimentally showing promising results on the standard CIFAR-10 and ImageNet datasets.
  arXiv  Detail & Related papers  (2024-04-26T12:43:19Z)
• Negative Pre-aware for Noisy Cross-modal Matching [46.5591267410225]
  Cross-modal noise-robust learning is a challenging task since noisy correspondence is hard to recognize and rectify. We present a novel Negative Pre-aware Cross-modal matching solution for large visual-language model fine-tuning on noisy downstream tasks.
  arXiv  Detail & Related papers  (2023-12-10T05:52:36Z)
• Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation [80.07065346699005]
  It is widely assumed that the optimal noise distribution should be made equal to the data distribution, as in Generative Adversarial Networks (GANs) We turn to Noise-Contrastive Estimation which grounds this self-supervised task as an estimation problem of an energy-based model of the data. We soberly conclude that the optimal noise may be hard to sample from, and the gain in efficiency can be modest compared to choosing the noise distribution equal to the data's.
  arXiv  Detail & Related papers  (2023-01-23T19:57:58Z)
• Improve Noise Tolerance of Robust Loss via Noise-Awareness [60.34670515595074]
  We propose a meta-learning method which is capable of adaptively learning a hyper parameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster for brevity) Four SOTA robust loss functions are attempted to be integrated with our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and performance.
  arXiv  Detail & Related papers  (2023-01-18T04:54:58Z)
• The Optimal Noise in Noise-Contrastive Learning Is Not What You Think [80.07065346699005]
  We show that deviating from this assumption can actually lead to better statistical estimators. In particular, the optimal noise distribution is different from the data's and even from a different family.
  arXiv  Detail & Related papers  (2022-03-02T13:59:20Z)
• Boosting Randomized Smoothing with Variance Reduced Classifiers [4.110108749051657]
  We motivate why ensembles are a particularly suitable choice as base models for Randomized Smoothing (RS) We empirically confirm this choice, obtaining state of the art results in multiple settings.
  arXiv  Detail & Related papers  (2021-06-13T08:40:27Z)
• Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition [45.858039215825656]
  We propose a new encoder that adopts globally attentive locally recurrent (GALR) networks and directly takes raw waveform as input. Experiments are conducted on a benchmark dataset AISHELL-2 and two large-scale Mandarin speech corpus of 5,000 hours and 21,000 hours.
  arXiv  Detail & Related papers  (2021-06-08T12:12:33Z)
• Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy [9.50143683501477]
  Insta-RS is a multiple-start search algorithm that assigns customized Gaussian variances to test examples. Insta-RS Train is a novel two-stage training algorithm that adaptively adjusts and customizes the noise level of each training example. We show that our method significantly enhances the average certified radius (ACR) as well as the clean data accuracy.
  arXiv  Detail & Related papers  (2021-03-07T19:46:07Z)
• ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning [91.13797346047984]
  We introduce ADAHESSIAN, a second order optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates. We show that ADAHESSIAN achieves new state-of-the-art results by a large margin as compared to other adaptive optimization methods.
  arXiv  Detail & Related papers  (2020-06-01T05:00:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.