Wasserstein distributional adversarial training for deep neural networks

Understand

Design of adversarial attacks for deep neural networks, as well as methods of adversarial training against them, are subject of intense research.

• In this paper, we propose methods to train against distributional attack threats, extending the TRADES method used for pointwise attacks.
• Our approach leverages recent contributions and relies on sensitivity analysis for Wasserstein distributionally robust optimization problems.
• We introduce an efficient fine-tuning method which can be deployed on a previously trained model.