このリポジトリは、LowProFoolのリポジトリを再現するためにDockerで再現するためにしたものです。
This repository is my hands-on repository from LowProFool by GithubLowProFool.

Disclaimer: This repository is not maintained anymore

LowProFool

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

Adverse.py

Contains the implementation of LowProFool [1] along with an modifier version of DeepFool [2] that handles tabular datasets.

Metrics.py

Implements metrics introduced in [1]

Playground.ipynb

A demo python notebook to generate adversarial examples on the German Credit dataset and compare the results to DeepFool

References

[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.

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