Adversarial Learning: A Critical Review and Active Learning Study
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2017.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 1.
6 papers.
- 2021A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?cited by 48
- 2022Poisoning Attacks and Defenses on Artificial Intelligence: A Surveycited by 15
- 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunitiescited by 8
- 2021Backdoor Attack and Defense for Deep Regressioncited by 6
- 2023Overcoming Adversarial Attacks for Human-in-the-Loop Applicationscited by 1
- 2021Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challengescited by 0
- 2023Overcoming Adversarial Attacks for Human-in-the-Loop Applications9 Jun 2023
- 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities17 Oct 2022
- 2022Poisoning Attacks and Defenses on Artificial Intelligence: A Survey21 Feb 2022
- 2021A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?1 Dec 2021
- 2021Backdoor Attack and Defense for Deep Regression6 Sep 2021
- 2021Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges30 Jun 2021