This code replicates the experiments in the paper "Defending Evasion Attacks via Adversarially Adaptive Training", published at IEEE Big Data 2022 (paper)

In this project, we use python 3.7.0 and dependencies:

• Install python 3.7.0: https://www.python.org/downloads/release/python-370/
• Install pip for python: https://pip.pypa.io/en/stable/installation/#
• Install dependencies: pip install -r pip-requirement.txt

Note: In order to avoid conflicts between the dependencies from other projects, we highly recommend using python virtual environment or Anaconda. The details can be found here: https://docs.python.org/3/tutorial/venv.html

To work on our source code, you might want to modify the following files:

• models.py Models that we used in AAD experiments.
• aad_cnn.py Functions to train adversarially adaptive model with MNIST dataset.
• aad_nn.py Functions to train adversarially adaptive model with COMPAS dataset.

To run the code aad_cnn.py and aad_nn.py, use terminal with these arguments:

`--lmd` Lambda value

`--gamma` Gamma value

`--seed` random seed

`--batch_size` batch size

`--meta_batch` number of meta batchs

`--lr` learning rate

`--gpu_id` gpu id for pytorch to use if available

`--epochs` number of training steps

`--model_name` name of the model to be saved

• data_processor.py Classes and functions to build training and test data that used in our scenarios.
• LR_baseline.py, CNN_pre_baseline.py and CNN_dec_baseline.py LR and CNN baselines in our experiments.
• test_white_box.py Test AAD model on white box scenario.

Due to file size limitation, we only include the source code for this project. The input data for this repo can be generated following the instructions in "Reproducibility_supplementary"
The full source code + data can be downloaded at downloaded at https://tinyurl.com/yxt5e869