# Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives

arXiv: https://arxiv.org/abs/2307.01390 · HTML: https://mlsanity.com/paper/2307.01390/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 28 of 28)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models](https://mlsanity.com/paper/1712.04248/) (2017)
- [Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering](https://mlsanity.com/paper/1811.03728/) (2018)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients](https://mlsanity.com/paper/1711.09404/) (2017)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Adversarial Training and Robustness for Multiple Perturbations](https://mlsanity.com/paper/1904.13000/) (2019)
- [EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples](https://mlsanity.com/paper/1709.04114/) (2017)
- [Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach](https://mlsanity.com/paper/1807.04457/) (2018)
- [Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data](https://mlsanity.com/paper/2101.08030/) (2021)
- [Adversarial Machine Learning in Network Intrusion Detection Systems](https://mlsanity.com/paper/2004.11898/) (2020)
- [Attacking the Madry Defense Model with L1-based Adversarial Examples](https://mlsanity.com/paper/1710.10733/) (2017)
- [SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark](https://mlsanity.com/paper/1709.08920/) (2017)
- [Adversarial Concept Drift Detection under Poisoning Attacks for Robust Data Stream Mining](https://mlsanity.com/paper/2009.09497/) (2020)
- [Real-Time Adversarial Attacks](https://mlsanity.com/paper/1905.13399/) (2019)
- [Online Adversarial Attacks](https://mlsanity.com/paper/2103.02014/) (2021)
- [Adaptive Stress Testing for Adversarial Learning in a Financial Environment](https://mlsanity.com/paper/2107.03577/) (2021)

## Built on by (most cited first, 5 of 5)

- [TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data](https://mlsanity.com/paper/2505.21027/) (2025)
- [Foe for Fraud: Transferable Adversarial Attacks in Credit Card Fraud Detection](https://mlsanity.com/paper/2508.14699/) (2025)
- [Layout-Aware Representation Learning for Open-Set ID Fraud Discovery](https://mlsanity.com/paper/2605.05215/) (2026)
- [FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection](https://mlsanity.com/paper/2502.02290/) (2025)
- [FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment](https://mlsanity.com/paper/2608.24551/) (2026)

## Abstract (arXiv)

Data economy relies on data-driven systems and complex machine learning applications are fueled by them. Unfortunately, however, machine learning models are exposed to fraudulent activities and adversarial attacks, which threaten their security and trustworthiness. In the last decade or so, the research interest on adversarial machine learning has grown significantly, revealing how learning applications could be severely impacted by effective attacks. Although early results of adversarial machine learning indicate the huge potential of the approach to specific domains such as image processing, still there is a gap in both the research literature and practice regarding how to generalize adversarial techniques in other domains and applications. Fraud detection is a critical defense mechanism for data economy, as it is for other applications as well, which poses several challenges for machine learning. In this work, we describe how attacks against fraud detection systems differ from other applications of adversarial machine learning, and propose a number of interesting directions to bridge this gap.