Presentation is available here
The results are located in the /Final Results folder and can be accessed using Plot-sample.ipynb.
To add more files with attack results, follow the given structure:
Dataset1
Model1
PGD
aa_res_Dataset1_100.csv
SimBA
aa_res_Dataset1_100.csv
Model2
PGD
aa_res_Dataset1_100.csv
SimBA
aa_res_Dataset1_100.csv
If you want to work with project with Docker, you can use folder docker_scripts.
Firstly copy file credentials_example to credentials and tune it with your variables for running docker. After you need to make docker image using command:
cd docker_scripts
bash build
For creating container run:
bash launch_container
All the requirements are listed in requirements.txt
For install all packages run
pip install -r requirements.txt
After you need to create folders checkpoints for saving classifier weights and results for saving adversarial attacks results.
Where are three basic steps: train classifier, attack model, train discriminator.
To run these steps you need to change assosiated config files in "config" folder and after that run assosiated python scrits train_classifier.py, attack_run.py and train_discriminator.py.
For example:
python train_classifier.py
The goal of the project is to create hardly detected adversarial attacks for time-series models.
| File or Folder | Content |
|---|---|
| checkpoints | folders for saving weights of the models |
| config | folder contains config files with params of models and paths |
| data | folder contains datasets For LSTM model (Ford_A) and datasets UCR, UEA datasets for TS2Vec models |
| docker_scripts | folder for set environment in docker container (if needed) |
| notebooks | folder with notebooks for data visualisation and small experiments |
| src | folder with code |