Adversarial training for tabular data with attack propagation

Understand

Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying fraudulent activity as legitimate, whereas system maintainers try to stop them.

• Adversarially training ML models that are robust against such attacks can prevent business losses and reduce the work load of system maintainers.
• In such applications data is often tabular and the space available for attackers to manipulate undergoes complex feature engineering transformations, to provide useful signals for model training, to a space attackers cannot access.
• Thus, we propose a new form of adversarial training where attacks are propagated between the two spaces in the training loop.