mlsanityData to 7 Oct 2026

Imperceptible Adversarial Attacks on Tabular Data

arXiv 1911.03274 ↗First posted 8 Nov 2019stat.ML cs.CR cs.LGcited by 35

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Abstract (arXiv)

Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions. While research on the topic has mainly been focusing on the image domain, numerous industrial applications, in particular in finance, rely on standard tabular data. In this paper, we discuss the notion of adversarial examples in the tabular domain. We propose a formalization based on the imperceptibility of attacks in the tabular domain leading to an approach to generate imperceptible adversarial examples. Experiments show that we can generate imperceptible adversarial examples with a high fooling rate.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2019.

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Citing papers per year: 2021: 8; 2022: 8; 2023: 7; 2024: 6; 2025: 6.

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  1. 2021Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysiscited by 52
  2. 2021Hard to Forget: Poisoning Attacks on Certified Machine Unlearningcited by 47
  3. 2021Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Datacited by 45
  4. 2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
  5. 2021Are socially-aware trajectory prediction models really socially-aware?cited by 32
  6. 2022On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbationscited by 32
  7. 2022Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbationcited by 28
  8. 2022A Survey of Adversarial Defences and Robustness in NLPcited by 22
  9. 2021Adversarial Robustness with Non-uniform Perturbationscited by 17
  10. 2022Adversarial Robustness for Tabular Data through Cost and Utility Awarenesscited by 14
  11. 2023Auditing and Generating Synthetic Data with Controllable Trust Trade-offscited by 13
  12. 2024Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Datacited by 11
23 more
  1. 2021Feature Importance Guided Attack: A Model Agnostic Adversarial Attackcited by 10
  2. 2021When and How to Fool Explainable Models (and Humans) with Adversarial Examplescited by 10
  3. 2022GenLabel: Mixup Relabeling using Generative Modelscited by 10
  4. 2024TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-casescited by 10
  5. 2024SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Gridscited by 5
  6. 2025Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlationscited by 5
  7. 2024Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysiscited by 4
  8. 2022Machine Learning in Access Control: A Taxonomy and Surveycited by 3
  9. 2023Adversarial Attacks on Tables with Entity Swapcited by 3
  10. 2023Artificial Neural Nets and the Representation of Human Conceptscited by 3
  11. 2024Trustworthy Actionable Perturbationscited by 3
  12. 2025On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defensescited by 3
  13. 2022Redactor: A Data-centric and Individualized Defense Against Inference Attackscited by 1
  14. 2024Model-Based Counterfactual Explanations Incorporating Feature Space Attributes for Tabular Datacited by 1
  15. 2025Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Datacited by 1
  16. 2021Generalized Adversarial Distances to Efficiently Discover Classifier Errorscited by 0
  17. 2023Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustnesscited by 0
  18. 2023Adaptive Adversarial Training Does Not Increase Recourse Costscited by 0
  19. 2023VeriDIP: Verifying Ownership of Deep Neural Networks through Privacy Leakage Fingerprintscited by 0
  20. 2023Backdoor Attacks on Transformers for Tabular Data: An Empirical Studycited by 0
  21. 2025Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networkscited by 0
  22. 2025Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Reviewcited by 0
  23. 2025Boundary on the Table: Efficient Black-Box Decision-Based Attacks for Structured Datacited by 0