Adversarial Robustness in Machine Learning

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.

How has Adversarial Robustness in Machine Learning's publication output changed over time?

Output grew50% over the shown period — from 2 works in 2018 to 3 in 2024.

What are the most-cited papers on Adversarial Robustness in Machine Learning?

Where is Adversarial Robustness in Machine Learning research published, and who funds it?

How much of the research on Adversarial Robustness in Machine Learning is open access?