Leveraging Generative Models for Combating Adversarial Attacks on Tabular Datasets

Author:

Zhou Jiahui,Zaidi Nayyar,Zhang Yishuo,Montague Paul,Kim Junae,Li Gang

Publisher

Springer Nature Switzerland

Reference13 articles.

1. Ballet, V., Renard, X., Aigrain, J., Laugel, T., Frossard, P., Detyniecki, M.: Imperceptible adversarial attacks on tabular data. arXiv preprint arXiv:1911.03274 (2019)

2. Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)

3. Li, Y.: Are generative classifiers more robust to adversarial attacks? CoRR abs/1802.06552 (2018), https://arxiv.org/abs/1802.06552

4. Liu, X., Hsieh, C.: From adversarial training to generative adversarial networks. CoRR abs/1807.10454 (2018), https://arxiv.org/abs/1807.10454

5. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)

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1. Improving neural network’s robustness on tabular data with D-layers;Data Mining and Knowledge Discovery;2023-08-31

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