mlsanityData to 7 Oct 2026

Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

arXiv 2101.08030 ↗First posted 20 Jan 2021cs.CR cs.AI cs.LGcited by 45

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Abstract (arXiv)

Guaranteeing the security of transactional systems is a crucial priority of all institutions that process transactions, in order to protect their businesses against cyberattacks and fraudulent attempts. Adversarial attacks are novel techniques that, other than being proven to be effective to fool image classification models, can also be applied to tabular data. Adversarial attacks aim at producing adversarial examples, in other words, slightly modified inputs that induce the Artificial Intelligence (AI) system to return incorrect outputs that are advantageous for the attacker. In this paper we illustrate a novel approach to modify and adapt state-of-the-art algorithms to imbalanced tabular data, in the context of fraud detection. Experimental results show that the proposed modifications lead to a perfect attack success rate, obtaining adversarial examples that are also less perceptible when analyzed by humans. Moreover, when applied to a real-world production system, the proposed techniques shows the possibility of posing a serious threat to the robustness of advanced AI-based fraud detection procedures.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 20 Jan 2021.

Builds on

21 of 30 references.

References · 21

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  6. 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
  7. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
  8. 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
  9. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  10. 2019HopSkipJumpAttack: A Query-Efficient Decision-Based Attackcited by 328
  11. 2017Adversarial Attacks on Neural Network Policiescited by 314
  12. 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
9 more
  1. 2018Adversarial Robustness Toolbox v1.0.0cited by 214
  2. 2017On the (Statistical) Detection of Adversarial Examplescited by 180
  3. 2016Technical Report on the CleverHans v2.1.0 Adversarial Examples Librarycited by 123
  4. 2018Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examplescited by 93
  5. 2019advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorchcited by 69
  6. 2019Imperceptible Adversarial Attacks on Tabular Datacited by 35
  7. 2020PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecardscited by 16
  8. 2017SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Sparkcited by 14
  9. 2020Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examplescited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 9; 2023: 10; 2024: 10; 2025: 9; 2026: 2.

45 papers.

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  1. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  2. 2025TabICL: A Tabular Foundation Model for In-Context Learning on Large Datacited by 267
  3. 2021Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysiscited by 52
  4. 2022Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbationcited by 28
  5. 2022On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attackscited by 15
  6. 2022Adversarial Robustness for Tabular Data through Cost and Utility Awarenesscited by 14
  7. 2023Auditing and Generating Synthetic Data with Controllable Trust Trade-offscited by 13
  8. 2024Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Datacited by 11
  9. 2021Feature Importance Guided Attack: A Model Agnostic Adversarial Attackcited by 10
  10. 2021When and How to Fool Explainable Models (and Humans) with Adversarial Examplescited by 10
  11. 2022GenLabel: Mixup Relabeling using Generative Modelscited by 10
  12. 2023GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Datacited by 10
33 more
  1. 2024TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-casescited by 10
  2. 2023Asymmetric Certified Robustness via Feature-Convex Neural Networkscited by 8
  3. 2022Class-Imbalanced Complementary-Label Learning via Weighted Losscited by 7
  4. 2022(De-)Randomized Smoothing for Decision Stump Ensemblescited by 5
  5. 2022Meta-Learning for Unsupervised Outlier Detection with Optimal Transportcited by 5
  6. 2023Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectivescited by 5
  7. 2023TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecturecited by 4
  8. 2024Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysiscited by 4
  9. 2025TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Datacited by 4
  10. 2025Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systemscited by 4
  11. 2021Bi-Discriminator Class-Conditional Tabular GANcited by 3
  12. 2022Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputscited by 3
  13. 2023Adversarial Attacks on Tables with Entity Swapcited by 3
  14. 2024Trustworthy Actionable Perturbationscited by 3
  15. 2024On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMscited by 2
  16. 2025Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologiescited by 2
  17. 2025Foe for Fraud: Transferable Adversarial Attacks in Credit Card Fraud Detectioncited by 2
  18. 2025Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversamplingcited by 2
  19. 2023Chaotic Variational Auto encoder-based Adversarial Machine Learningcited by 1
  20. 2023Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Datacited by 1
  21. 2024MISLEAD: Manipulating Importance of Selected features for Learning Epsilon in Evasion Attack Deceptioncited by 1
  22. 2024Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysiscited by 1
  23. 2026Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applicationscited by 1
  24. 2022Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systemscited by 0
  25. 2023Adversarial training for tabular data with attack propagationcited by 0
  26. 2023Designing an attack-defense game: how to increase robustness of financial transaction models via a competitioncited by 0
  27. 2024Towards White Box Deep Learningcited by 0
  28. 2024Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistencycited by 0
  29. 2024Iterative Feature Exclusion Ranking for Deep Tabular Learningcited by 0
  30. 2025MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selectioncited by 0
  31. 2025Boundary on the Table: Efficient Black-Box Decision-Based Attacks for Structured Datacited by 0
  32. 2025CatBack: Universal Backdoor Attacks on Tabular Data via Categorical Encodingcited by 0
  33. 2026FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessmentcited by 0