Deep generative models as an adversarial attack strategy for tabular machine learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912034926362624 |
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| author | Dyrmishi, Salijona Stoian, Mihaela Cătălina Giunchiglia, Eleonora Cordy, Maxime |
| author_facet | Dyrmishi, Salijona Stoian, Mihaela Cătălina Giunchiglia, Eleonora Cordy, Maxime |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12642 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep generative models as an adversarial attack strategy for tabular machine learning Dyrmishi, Salijona Stoian, Mihaela Cătălina Giunchiglia, Eleonora Cordy, Maxime Machine Learning Artificial Intelligence 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. |
| title | Deep generative models as an adversarial attack strategy for tabular machine learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2409.12642 |