# Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers

arXiv: https://arxiv.org/abs/2109.10770 · HTML: https://mlsanity.com/paper/2109.10770/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 23 of 23)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [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)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [High Accuracy and High Fidelity Extraction of Neural Networks](https://mlsanity.com/paper/1909.01838/) (2019)
- [Adversarial Active Learning for Deep Networks: a Margin Based Approach](https://mlsanity.com/paper/1802.09841/) (2018)
- [Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach](https://mlsanity.com/paper/1807.04457/) (2018)
- [Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction](https://mlsanity.com/paper/1904.01690/) (2019)
- [Exploring Connections Between Active Learning and Model Extraction](https://mlsanity.com/paper/1811.02054/) (2018)
- [Model-Reuse Attacks on Deep Learning Systems](https://mlsanity.com/paper/1812.00483/) (2018)
- [Rates of Convergence for Nearest Neighbor Classification](https://mlsanity.com/paper/1407.0067/) (2014)
- [Diving into the shallows: a computational perspective on large-scale shallow learning](https://mlsanity.com/paper/1703.10622/) (2017)
- [A representer theorem for deep kernel learning](https://mlsanity.com/paper/1709.10441/) (2017)
- [Hierarchical Novelty Detection for Visual Object Recognition](https://mlsanity.com/paper/1804.00722/) (2018)
- [Very Deep Convolutional Networks for Text Classification](https://mlsanity.com/paper/1606.01781/) (2016)
- [Robustness for Non-Parametric Classification: A Generic Attack and Defense](https://mlsanity.com/paper/1906.03310/) (2019)
- [Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains](https://mlsanity.com/paper/1703.07909/) (2017)

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

- [Understanding Overfitting in Adversarial Training via Kernel Regression](https://mlsanity.com/paper/2304.06326/) (2023)

## Abstract (arXiv)

Machine learning researchers have long noticed the phenomenon that the model training process will be more effective and efficient when the training samples are densely sampled around the underlying decision boundary. While this observation has already been widely applied in a range of machine learning security techniques, it lacks theoretical analyses of the correctness of the observation. To address this challenge, we first add particular perturbation to original training examples using adversarial attack methods so that the generated examples could lie approximately on the decision boundary of the ML classifiers. We then investigate the connections between active learning and these particular training examples. Through analyzing various representative classifiers such as k-NN classifiers, kernel methods as well as deep neural networks, we establish a theoretical foundation for the observation. As a result, our theoretical proofs provide support to more efficient active learning methods with the help of adversarial examples, contrary to previous works where adversarial examples are often used as destructive solutions. Experimental results show that the established theoretical foundation will guide better active learning strategies based on adversarial examples.