M. Mridha

A Two-Stage Adversarial Defense Architecture for Robust Fraud Detection on Imbalanced Financial Data

This paper proposes a unified dual-defense framework that jointly integrates adversarial training with a denoising autoencoder (DAE)-based filtering mechanism, specifically designed for imbalanced tabular financial data under adversarial conditions, and explicitly targets adversarial robustness in financial fraud detection.