CS420 Bonus

Bonus on Adversarial Machine Learning in final project of CS420 in SJTU.

Based on pre-trained model, we apply AdverTorch to attack our classifier and defense.

Particularly, we perform untargeted attack and construct defenses based on preprocessing.

How to use

Install PyTorch

For Linux:

conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

Refer to Pytorch.

Install AdverTorch

pip install advertorch

Refer to AdverTorch

Install other packages

Import packages in python files.

Files

Python files

adversarial_defense.py should run first.

• adversarial_defense.py is to perform attack and defense, and generate adversarial as well as defended data files.
• accuracy.py is an accuracy test on all three kinds of data.
• comparison.py is a visulization of prediction and comparison results.

Data files

First create a data folder.

Dataset

Download Facial Expression Recognition Dataset and add fer2013.csv file to data folder.

Generate other data

Run adversarial_defense.py to generate:

• cln_defended.txt: defended clean data;
• adv.txt: adversarial data;
• adv_defended.txt: defended adversarial data.

Model files

In models folder, there are pre-trained model and network. Refer to Project.

Acknowledgement

Thanks to my collaborator JiaYi, course TAs, and our teacher Tu Shikui.