# Adversarial Learning: A Critical Review and Active Learning Study

arXiv: https://arxiv.org/abs/1705.09823 · HTML: https://mlsanity.com/paper/1705.09823/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2017.

## Built on by (most cited first, 7 of 7)

- [A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?](https://mlsanity.com/paper/2112.00639/) (2021)
- [Poisoning Attacks and Defenses on Artificial Intelligence: A Survey](https://mlsanity.com/paper/2202.10276/) (2022)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [Backdoor Attack and Defense for Deep Regression](https://mlsanity.com/paper/2109.02381/) (2021)
- [New data poison attacks on machine learning classifiers for mobile exfiltration](https://mlsanity.com/paper/2210.11592/) (2022)
- [Overcoming Adversarial Attacks for Human-in-the-Loop Applications](https://mlsanity.com/paper/2306.05952/) (2023)
- [Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges](https://mlsanity.com/paper/2107.02894/) (2021)

## Abstract (arXiv)

This papers consists of two parts. The first is a critical review of prior art on adversarial learning, identifying some significant limitations of previous works. The second part is an experimental study considering adversarial active learning and an investigation of the efficacy of a mixed sample selection strategy for combating an adversary who attempts to disrupt the classifier learning.