Improving the Generalization of Adversarial Training with Domain Adaptation

Understand

By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models.

• However, most existing adversarial training approaches are based on a specific type of adversarial attack.
• It may not provide sufficiently representative samples from the adversarial domain, leading to a weak generalization ability on adversarial examples from other attacks.
• Moreover, during the adversarial training, adversarial perturbations on inputs are usually crafted by fast single-step adversaries so as to scale to large datasets.