Search Results for author:
Found 2 papers, 0 papers with code
Improving Robust Fairness via Balance Adversarial Training
Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes, known as the robust fairness problem.
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models
These fine-grained adversarial examples can be used for selecting robust backbone networks and auxiliary features.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
