# Imperceptible Adversarial Attacks on Tabular Data

arXiv: https://arxiv.org/abs/1911.03274 · HTML: https://mlsanity.com/paper/1911.03274/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 30 of 35)

- [Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis](https://mlsanity.com/paper/2106.09992/) (2021)
- [Hard to Forget: Poisoning Attacks on Certified Machine Unlearning](https://mlsanity.com/paper/2109.08266/) (2021)
- [Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data](https://mlsanity.com/paper/2101.08030/) (2021)
- [Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview](https://mlsanity.com/paper/2206.07438/) (2022)
- [Are socially-aware trajectory prediction models really socially-aware?](https://mlsanity.com/paper/2108.10879/) (2021)
- [On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations](https://mlsanity.com/paper/2201.09051/) (2022)
- [Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation](https://mlsanity.com/paper/2212.09994/) (2022)
- [A Survey of Adversarial Defences and Robustness in NLP](https://mlsanity.com/paper/2203.06414/) (2022)
- [Adversarial Robustness with Non-uniform Perturbations](https://mlsanity.com/paper/2102.12002/) (2021)
- [Adversarial Robustness for Tabular Data through Cost and Utility Awareness](https://mlsanity.com/paper/2208.13058/) (2022)
- [Auditing and Generating Synthetic Data with Controllable Trust Trade-offs](https://mlsanity.com/paper/2304.10819/) (2023)
- [Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data](https://mlsanity.com/paper/2406.00775/) (2024)
- [Feature Importance Guided Attack: A Model Agnostic Adversarial Attack](https://mlsanity.com/paper/2106.14815/) (2021)
- [When and How to Fool Explainable Models (and Humans) with Adversarial Examples](https://mlsanity.com/paper/2107.01943/) (2021)
- [GenLabel: Mixup Relabeling using Generative Models](https://mlsanity.com/paper/2201.02354/) (2022)
- [TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases](https://mlsanity.com/paper/2408.07579/) (2024)
- [SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids](https://mlsanity.com/paper/2407.12421/) (2024)
- [Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations](https://mlsanity.com/paper/2501.05588/) (2025)
- [Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis](https://mlsanity.com/paper/2407.11463/) (2024)
- [Machine Learning in Access Control: A Taxonomy and Survey](https://mlsanity.com/paper/2207.01739/) (2022)
- [Adversarial Attacks on Tables with Entity Swap](https://mlsanity.com/paper/2309.08650/) (2023)
- [Artificial Neural Nets and the Representation of Human Concepts](https://mlsanity.com/paper/2312.05337/) (2023)
- [Trustworthy Actionable Perturbations](https://mlsanity.com/paper/2405.11195/) (2024)
- [On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses](https://mlsanity.com/paper/2506.02978/) (2025)
- [Redactor: A Data-centric and Individualized Defense Against Inference Attacks](https://mlsanity.com/paper/2202.02902/) (2022)
- [Model-Based Counterfactual Explanations Incorporating Feature Space Attributes for Tabular Data](https://mlsanity.com/paper/2404.13224/) (2024)
- [Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data](https://mlsanity.com/paper/2507.10998/) (2025)
- [Generalized Adversarial Distances to Efficiently Discover Classifier Errors](https://mlsanity.com/paper/2102.12844/) (2021)
- [Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness](https://mlsanity.com/paper/2306.06081/) (2023)
- [Adaptive Adversarial Training Does Not Increase Recourse Costs](https://mlsanity.com/paper/2309.02528/) (2023)

## 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.