Adversarial Robustness in Machine Learning
Adversarial Robustness in Machine Learning is a research topic within Artificial Intelligence. Science Explorer counts 19k research works in it since 1964. 22.4% of them reached the world's top 10% most cited for their field and year.
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.
- Adversarial Examples
- Deep Learning
- Robustness
- Neural Networks
- Machine Learning
- Security
- Uncertainty Estimation
- Defenses
- Attack
- Verification
- Research works
- 19k fractional, since 1964
- In the world top 10%
- 4.3k per year above
- Top-10% rate
- 22.4% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +1032% the tick is no change
Which countries lead Adversarial Robustness in Machine Learning research?
By volume, China and the United States publish the most (3.2k and 2k works in 2022–2025).
By volume, 2022–2025
- 1 China 3.2k works
- 2 United States 2k works
- 3 India 760 works
- 4 Germany 442 works
- 5 United Kingdom 406 works
- 6 South Korea 283 works
- 7 Australia 272 works
- 8 Canada 267 works
- 9 Italy 263 works
- 10 Japan 236 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Adversarial Robustness in Machine Learning research?
By volume in 2022–2025, National University of Defense Technology publishes the most Adversarial Robustness in Machine Learning research, followed by Tsinghua University and Shanghai Jiao Tong University.
By volume, 2022–2025
- 1 National University of Defense Technology China 83 works
- 2 Tsinghua University China 71 works
- 3 Shanghai Jiao Tong University China 71 works
- 4 Xidian University China 67 works
- 5 Nanyang Technological University Singapore 64 works
- 6 Chinese Academy of Sciences China 62 works
- 7 Zhejiang University China 61 works
- 8 Wuhan University China 60 works
- 9 University of Electronic Science and Technology of China China 60 works
- 10 Beihang University China 60 works
Who are the leading researchers in Adversarial Robustness in Machine Learning?
The most-cited researchers publishing on Adversarial Robustness in Machine Learning include Li Fei-Fei, Yoshua Bengio and Serge Belongie.
- 1 Li Fei-Fei 17k citations
- 2 Yoshua Bengio 17k citations
- 3 Serge Belongie 14k citations
- 4 Olga Russakovsky 8.9k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Adversarial Robustness in Machine Learning research done?
The largest centres of Adversarial Robustness in Machine Learning research in 2022–2025 are Beijing (China), Shanghai (China), Nanjing (China) and Xi'an (China).
Where is the best place to study Adversarial Robustness in Machine Learning?
Among universities, judged by research, Nanyang Technological University, Carnegie Mellon University and Singapore University of Technology and Design score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Nanyang Technological University Singapore | 77.8 | 31.8% | 8.0× | 64 | +409.0% |
| 2 | Carnegie Mellon University United States | 66.3 | 28.2% | 8.3× | 34 | +659.0% |
| 3 | Singapore University of Technology and Design Singapore | 64.8 | 45.1% | 9.6× | 9 | — |
| 4 | Singapore Management University Singapore | 64.7 | 35.5% | 16.5× | 18 | — |
| 5 | City University of Macau Macau | 63.6 | 38.6% | 8.9× | 14 | — |
| 6 | Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates | 63.3 | 35.8% | 24.2× | 10 | — |
| 7 | Xidian University China | 63.2 | 27.3% | 9.0× | 66 | — |
| 8 | Hong Kong University of Science and Technology Hong Kong | 61.2 | 35.5% | 6.8× | 26 | — |
| 9 | National University of Defense Technology China | 58.8 | 14.7% | 10.7× | 83 | — |
| 10 | University of Technology Sydney Australia | 58.4 | 34.7% | 6.5× | 29 | — |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Adversarial Robustness in Machine Learning research growing?
Output in 2018–2022 was 1032% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Adversarial Robustness in Machine Learning.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.