# Adversarial Robustness in Machine Learning

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/adversarial-robustness-in-machine-learning-2/

## Facts

| Field | Value |
| --- | --- |
| Citations | 560,219 |
| Description | This cluster of papers focuses on the robustness of deep learning models against adversarial attacks, exploring topics such as adversarial examples, security, uncertainty estimation, defenses, and verification. It delves into the challenges and potential solutions for ensuring the resilience of neural networks in the face of malicious inputs. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | https://openalex.org/T11689 |
| Works | 66,394 |

## Topic researchers

Showing 12 of 20.

- [Kaiming He](https://scholariq.org/researchers/kaiming-he/)
- [Yoshua Bengio](https://scholariq.org/researchers/yoshua-bengio/)
- [Geoffrey E. Hinton](https://scholariq.org/researchers/geoffrey-e-hinton/)
- [Daniel Kahneman](https://scholariq.org/researchers/daniel-kahneman/)
- [Shaoqing Ren](https://scholariq.org/researchers/shaoqing-ren/)
- [Ross Girshick](https://scholariq.org/researchers/ross-girshick/)
- [Xiangyu Zhang](https://scholariq.org/researchers/xiangyu-zhang/)
- [Jian Sun](https://scholariq.org/researchers/jian-sun-2/)
- [Yann LeCun](https://scholariq.org/researchers/yann-lecun/)
- [Li Fei-Fei](https://scholariq.org/researchers/li-fei-fei/)
- [Ilya Sutskever](https://scholariq.org/researchers/ilya-sutskever/)
- [Trevor Darrell](https://scholariq.org/researchers/trevor-darrell/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.