Deep generative models as an adversarial attack strategy for tabular machine learning
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Abstract (arXiv)
Deep Generative Models (DGMs) have found application in computer vision for generating adversarial examples to test the robustness of machine learning (ML) systems. Extending these adversarial techniques to tabular ML presents unique challenges due to the distinct nature of tabular data and the necessity to preserve domain constraints in adversarial examples. In this paper, we adapt four popular tabular DGMs into adversarial DGMs (AdvDGMs) and evaluate their effectiveness in generating realistic adversarial examples that conform to domain constraints.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 24 references that are in the map. First posted on arXiv 19 Sep 2024.
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18 of 24 references.
References · 18
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- 2017Wasserstein GANcited by 639
- 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 377
- 2018Generating Adversarial Examples with Adversarial Networkscited by 258
- 2018Data Synthesis based on Generative Adversarial Networkscited by 185
- 2018Constructing Unrestricted Adversarial Examples with Generative Modelscited by 135
- 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
- 2022STaSy: Score-based Tabular data Synthesiscited by 86
- 2017Generating Adversarial Malware Examples for Black-Box Attacks Based on GANcited by 75
- 2018IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detectioncited by 42
- 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
- 2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
6 more
- 2022Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesiscited by 24
- 2020AI-GAN: Attack-Inspired Generation of Adversarial Examplescited by 22
- 2021A Unified Framework for Adversarial Attack and Defense in Constrained Feature Spacecited by 22
- 2019AdvGAN++ : Harnessing latent layers for adversary generationcited by 20
- 2020AdvFoolGen: Creating Persistent Troubles for Deep Classifierscited by 2
- 2023Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Datacited by 1
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