Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training

• URL: http://arxiv.org/abs/2506.04263v1
• Date: Tue, 03 Jun 2025 04:18:53 GMT
• Title: Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training
• Authors: Alan Mitkiy, James Smith, Hana Satou, Hiroshi Tanaka, Emily Johnson, F Monkey,
• Abstract summary: Adversarial training is one of the most effective strategies for defending neural networks against adversarial examples.<n>Existing adversarial training approaches rely on a fixed perturbation budget, which fails to account for robustness-specific characteristics.<n>We propose Dynamic Epsilon Scheduling (DES), a novel framework that adaptively adjusts the adversarial perturbation budget per instance and per training instance.
• Score: 1.5558386948322986
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Adversarial training is among the most effective strategies for defending deep neural networks against adversarial examples. A key limitation of existing adversarial training approaches lies in their reliance on a fixed perturbation budget, which fails to account for instance-specific robustness characteristics. While prior works such as IAAT and MMA introduce instance-level adaptations, they often rely on heuristic or static approximations of data robustness. In this paper, we propose Dynamic Epsilon Scheduling (DES), a novel framework that adaptively adjusts the adversarial perturbation budget per instance and per training iteration. DES integrates three key factors: (1) the distance to the decision boundary approximated via gradient-based proxies, (2) prediction confidence derived from softmax entropy, and (3) model uncertainty estimated via Monte Carlo dropout. By combining these cues into a unified scheduling strategy, DES tailors the perturbation budget dynamically to guide more effective adversarial learning. Experimental results on CIFAR-10 and CIFAR-100 show that our method consistently improves both adversarial robustness and standard accuracy compared to fixed-epsilon baselines and prior adaptive methods. Moreover, we provide theoretical insights into the stability and convergence of our scheduling policy. This work opens a new avenue for instance-aware, data-driven adversarial training methods.

Related papers

• Adversarial Training in Low-Label Regimes with Margin-Based Interpolation [8.585017175426023]
  Adversarial training has emerged as an effective approach to train robust neural network models that are resistant to adversarial attacks.<n>In this paper, we introduce a novel semi-supervised adversarial training approach that enhances both robustness and natural accuracy.
  arXiv  Detail & Related papers  (2024-11-27T00:35:13Z)
• Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks [11.389689242531327]
  Adversarial training is one of the most effective methods for enhancing model robustness. Previous approaches primarily use static ground truth for adversarial training, but this often causes robust overfitting. We propose a dynamic label adversarial training (DYNAT) algorithm that enables the target model to gain robustness from the guide model's decisions.
  arXiv  Detail & Related papers  (2024-08-23T14:25:12Z)
• Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space [3.639580365066386]
  We propose an adaptive adversarial coefficient framework to adjust the effect of the adversarial perturbation during training. The appealing feature of our method is that it is simple to deploy in real-world applications and does not require accessing the simulator in advance. The experiments in MuJoCo show that our method can improve the training stability and learn a robust policy when migrated to different test environments.
  arXiv  Detail & Related papers  (2024-05-20T12:31:11Z)
• Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization [53.04697800214848]
  Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models. AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting. We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
  arXiv  Detail & Related papers  (2023-10-19T13:13:41Z)
• Doubly Robust Instance-Reweighted Adversarial Training [107.40683655362285]
  We propose a novel doubly-robust instance reweighted adversarial framework. Our importance weights are obtained by optimizing the KL-divergence regularized loss function. Our proposed approach outperforms related state-of-the-art baseline methods in terms of average robust performance.
  arXiv  Detail & Related papers  (2023-08-01T06:16:18Z)
• Strength-Adaptive Adversarial Training [103.28849734224235]
  Adversarial training (AT) is proven to reliably improve network's robustness against adversarial data. Current AT with a pre-specified perturbation budget has limitations in learning a robust network. We propose emphStrength-Adaptive Adversarial Training (SAAT) to overcome these limitations.
  arXiv  Detail & Related papers  (2022-10-04T00:22:37Z)
• Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy [41.84118016227271]
  This paper proposes an attack-independent (non-adversarial training) technique for improving adversarial robustness of neural network models. We suggest creating a neighborhood around each training example, such that the label is kept constant for all inputs within that neighborhood. Results suggest that the proposed approach improves standard accuracy over other defenses while having increased robustness compared to vanilla adversarial training.
  arXiv  Detail & Related papers  (2021-03-15T10:44:59Z)
• A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning [90.44219200633286]
  We propose a simple yet very effective adversarial fine-tuning approach based on a $textitslow start, fast decay$ learning rate scheduling strategy. Experimental results show that the proposed adversarial fine-tuning approach outperforms the state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet datasets.
  arXiv  Detail & Related papers  (2020-12-25T20:50:15Z)
• Self-Progressing Robust Training [146.8337017922058]
  Current robust training methods such as adversarial training explicitly uses an "attack" to generate adversarial examples. We propose a new framework called SPROUT, self-progressing robust training. Our results shed new light on scalable, effective and attack-independent robust training methods.
  arXiv  Detail & Related papers  (2020-12-22T00:45:24Z)
• Robust Pre-Training by Adversarial Contrastive Learning [120.33706897927391]
  Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness. We improve robustness-aware self-supervised pre-training by learning representations consistent under both data augmentations and adversarial perturbations.
  arXiv  Detail & Related papers  (2020-10-26T04:44:43Z)
• Adversarial Distributional Training for Robust Deep Learning [53.300984501078126]
  Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples. Most existing AT methods adopt a specific attack to craft adversarial examples, leading to the unreliable robustness against other unseen attacks. In this paper, we introduce adversarial distributional training (ADT), a novel framework for learning robust models.
  arXiv  Detail & Related papers  (2020-02-14T12:36:59Z)

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.