# TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases

arXiv: https://arxiv.org/abs/2408.07579 · HTML: https://mlsanity.com/paper/2408.07579/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 24 of 24)

- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [On Adaptive Attacks to Adversarial Example Defenses](https://mlsanity.com/paper/2002.08347/) (2020)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://mlsanity.com/paper/2306.11698/) (2023)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [PacGAN: The power of two samples in generative adversarial networks](https://mlsanity.com/paper/1712.04086/) (2017)
- [Benchmarking Distribution Shift in Tabular Data with TableShift](https://mlsanity.com/paper/2312.07577/) (2023)
- [Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data](https://mlsanity.com/paper/2011.03274/) (2020)
- [Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data](https://mlsanity.com/paper/2101.08030/) (2021)
- [Imperceptible Adversarial Attacks on Tabular Data](https://mlsanity.com/paper/1911.03274/) (2019)
- [How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data](https://mlsanity.com/paper/2402.04823/) (2024)
- [FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments](https://mlsanity.com/paper/1909.10480/) (2019)
- [A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space](https://mlsanity.com/paper/2112.01156/) (2021)
- [Towards Benchmark Datasets for Machine Learning Based Website Phishing Detection: An experimental study](https://mlsanity.com/paper/2010.12847/) (2020)
- [On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks](https://mlsanity.com/paper/2202.03277/) (2022)
- [Adversarial Robustness for Tabular Data through Cost and Utility Awareness](https://mlsanity.com/paper/2208.13058/) (2022)
- [A LLM Assisted Exploitation of AI-Guardian](https://mlsanity.com/paper/2307.15008/) (2023)
- [Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data](https://mlsanity.com/paper/2406.00775/) (2024)
- [Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs](https://mlsanity.com/paper/2212.06836/) (2022)

## Built on by (most cited first, 10 of 10)

- [CLIMB: Class-imbalanced Learning Benchmark on Tabular Data](https://mlsanity.com/paper/2505.17451/) (2025)
- [TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data](https://mlsanity.com/paper/2505.21027/) (2025)
- [On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses](https://mlsanity.com/paper/2506.02978/) (2025)
- [Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data](https://mlsanity.com/paper/2507.10998/) (2025)
- [Privacy Vulnerabilities of Attention Layers in Tabular Foundation Models and Protection of High-Risk Queries](https://mlsanity.com/paper/2606.26021/) (2026)
- [Table2Image: Lightweight Tabular Learning with Generated Proxy Representations and Reliability Diagnostics](https://mlsanity.com/paper/2412.06265/) (2024)
- [Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories](https://mlsanity.com/paper/2506.12911/) (2025)
- [Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review](https://mlsanity.com/paper/2506.15506/) (2025)
- [FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment](https://mlsanity.com/paper/2608.24551/) (2026)
- [Counterfactual Fragility Certificates: Exposing High-Confidence Brittleness under Structured Evidence Failure](https://mlsanity.com/paper/2609.00366/) (2026)

## Abstract (arXiv)

While adversarial robustness in computer vision is a mature research field, fewer researchers have tackled the evasion attacks against tabular deep learning, and even fewer investigated robustification mechanisms and reliable defenses. We hypothesize that this lag in the research on tabular adversarial attacks is in part due to the lack of standardized benchmarks. To fill this gap, we propose TabularBench, the first comprehensive benchmark of robustness of tabular deep learning classification models. We evaluated adversarial robustness with CAA, an ensemble of gradient and search attacks which was recently demonstrated as the most effective attack against a tabular model. In addition to our open benchmark (https://github.com/serval-uni-lu/tabularbench) where we welcome submissions of new models and defenses, we implement 7 robustification mechanisms inspired by state-of-the-art defenses in computer vision and propose the largest benchmark of robust tabular deep learning over 200 models across five critical scenarios in finance, healthcare and security. We curated real datasets for each use case, augmented with hundreds of thousands of realistic synthetic inputs, and trained and assessed our models with and without data augmentations. We open-source our library that provides API access to all our pre-trained robust tabular models, and the largest datasets of real and synthetic tabular inputs. Finally, we analyze the impact of various defenses on the robustness and provide actionable insights to design new defenses and robustification mechanisms.