# Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation

arXiv: https://arxiv.org/abs/2408.07364 · HTML: https://mlsanity.com/paper/2408.07364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack](https://mlsanity.com/paper/1907.02044/) (2019)
- [Generative Adversarial Active Learning](https://mlsanity.com/paper/1702.07956/) (2017)
- [Dynamic Adversarial Attacks on Autonomous Driving Systems](https://mlsanity.com/paper/2312.06701/) (2023)
- [Stabilizing Elastic Weight Consolidation method in practical ML tasks and using weight importances for neural network pruning](https://mlsanity.com/paper/2109.10021/) (2021)
- [M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks](https://mlsanity.com/paper/2306.12376/) (2023)

## Built on by (most cited first, 1 of 1)

- [Attack and defense techniques in large language models: A survey and new perspectives](https://mlsanity.com/paper/2505.00976/) (2025)

## Abstract (arXiv)

Despite significant advancements in active learning and adversarial attacks, the intersection of these two fields remains underexplored, particularly in developing robust active learning frameworks against dynamic adversarial threats. The challenge of developing robust active learning frameworks under dynamic adversarial attacks is critical, as these attacks can lead to catastrophic forgetting within the active learning cycle. This paper introduces Robust Active Learning (RoAL), a novel approach designed to address this issue by integrating Elastic Weight Consolidation (EWC) into the active learning process. Our contributions are threefold: First, we propose a new dynamic adversarial attack that poses significant threats to active learning frameworks. Second, we introduce a novel method that combines EWC with active learning to mitigate catastrophic forgetting caused by dynamic adversarial attacks. Finally, we conduct extensive experimental evaluations to demonstrate the efficacy of our approach. The results show that RoAL not only effectively counters dynamic adversarial threats but also significantly reduces the impact of catastrophic forgetting, thereby enhancing the robustness and performance of active learning systems in adversarial environments.