Disclaimer: This repository is not maintained anymore
LowProFool is an algorithm that generates imperceptible adversarial examples
This GitHub hosts the code to replicate the experiments presented in the paper:
Ballet, V., Renard, X., Aigrain, J., Laugel, T., Frossard, P., & Detyniecki, M. (2019). Imperceptible Adversarial Attacks on Tabular Data. NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy (Robust AI in FS 2019)
https://arxiv.org/abs/1911.03274
Contains the implementation of LowProFool [1] along with an modifier version of DeepFool [2] that handles tabular datasets.
Implements metrics introduced in [1]
A demo python notebook to generate adversarial examples on the German Credit dataset and compare the results to DeepFool
[1] Ballet, V., Renard, X., Aigrain, J., Laugel, T., Frossard, P., & Detyniecki, M. (2019). Imperceptible Adversarial Attacks on Tabular Data. NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy (Robust AI in FS 2019)
[2] S. Moosavi-Dezfooli, A. Fawzi, P. Frossard: DeepFool: a simple and accurate method to fool deep neural networks. In Computer Vision and Pattern Recognition (CVPR ’16), IEEE, 2016.