This is the repository for our paper "Towards Defending Multiple $\ell_{p}$-norm Bounded Adversarial Perturbations via Gated Batch Normalization".

Prerequisites

Create anaconda environment.

conda create -n gbn python=3.6
conda activate gbn

Install requirements.

pip install -r requirements.txt

Training

Train an adversarial defensed LeNet5 model with GBN module, and test its accuracy under PGD $\ell_{1}$, $\ell_{2}$, and $\ell_{\infty}$ attack:

python train_gbn.py

Train a vanilla LeNet5 model without any adversarial attacks:

python train_lenet_vanilla.py

Train an adversarial defensed model using the average loss of $\ell_{1}$, $\ell_{2}$, and $\ell_{\infty}$ PGD adversarial attack:

python train_lenet_AVG.py