Open access
2026
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.
Mohammed Saad Javeed, Jannatul Maua, M. Mridha et al.
· Computers, Materials & C... · 0 citations