This repository contains implementations of various adversarial defense methods, including **AIR**, **Vanilla Adversarial Training (AT)**, **LFL**, **EWC**, **Feat. Extraction** and **Joint Training**.
The code also allows training on multiple tasks (attacks) sequentially, with automatic defense method selection from the configuration file.
## Setup & Installation
### Prerequisites
To run this code, you’ll need to install the following dependencies:
1.**Python 3.x**
2.**PyTorch** (for training models)
3.**NumPy** (for numerical computations)
4.**torchvision** (for image transformations and datasets)
5.**Matplotlib** (for plotting and visualization)
6.**scikit-learn** (for t-SNE visualization)
### Step-by-step setup
1.**Clone the repository:**
First, clone the repository to your local machine:
It's recommended to use a virtual environment to isolate the dependencies.
```bash
python -m venv venv
source venv/bin/activate
```
3.**Install dependencies:**
Install the necessary dependencies using `pip`:
```bash
pip install-r requirements.txt
```
4.**Configure the environment:**
Set up the configuration file (`Config`) in the Python code to specify your training parameters, dataset, and attack/defense methods.
---
## Usage Instructions
### Running the code
After setting up the environment, you can start training your model using the following command:
```bash
python main.py
```
This will start training on the dataset and attacks specified in the `Config` class, which is located in `utils/config.py`. You can modify the config file to specify different training parameters, attack methods, defense methods, and dataset.
### Configuration Options
The configuration options can be found in `utils/config.py`. Some of the key parameters you can adjust include:
-**Dataset**: Choose between `MNIST`, `CIFAR10`, and `CIFAR100`.
-**Attack Methods**: Specify the sequence of attacks you want to train on (e.g., `FGSM`, `PGD`, or `None`).
-**Defense Methods**: Choose a defense method like `AIR`, `LFL`, `JointTraining`,`VanillaAT`, `FeatExtraction`,`EWC`, etc.
-**Training Parameters**: You can adjust `epochs`, `batch_size`, `learning_rate`, and other parameters as needed.
### Configuration Example
Below is an example of the configuration you might define in `config.py`:
```python
@dataclass
classConfig:
# General
seed:int=42
device:str="cuda"
# Training
epochs:int=30
batch_size:int=128
learning_rate:float=0.1
momentum:float=0.9
weight_decay:float=5e-4
# Attack params
epsilon:float=8/255
alpha:float=2/255
num_steps:int=10
random_init:bool=True
# Defense selection
defense_method:str="AIR"
# AIR parameters
lambda_SD:float=1.0
lambda_IR:float=1.0
lambda_AR:float=1.0
lambda_Reg:float=1.0
alpha_range:tuple=(0.3,0.7)
use_rdrop:bool=True
# Isotropic replay augmentations
iso_noise_std:float=0.01
iso_clamp_min:float=0.0
iso_clamp_max:float=1.0
iso_p_flip:float=0.5
iso_flip_dim:int=3
iso_p_rotation:float=0.5
iso_max_rotation:int=10
iso_p_crop:float=0.5
iso_p_erase:float=0.5
# Dataset
dataset:str="MNIST"
data_root:str="./data"
num_workers:int=2
# LFL
lambda_lfl:float=1.0
feature_lambda:float=1.0
freeze_classifier:bool=True
# JointTraining
joint_lambda:float=0.5
# VanillaAT
adv_lambda:float=1.0
# FeatExtraction
feat_lambda:float=1.0
noise_std:float=0.01
# EWC
lambda_ewc:float=100.0
# Multi-task or multi-attack scenario
attack_sequence:tuple=("FGSM","PGD")
```
You can modify `attack_sequence` and `defense_method` as per your needs.
---
## Results
After training, the results will be saved and displayed. You can see:
-**Clean Accuracy**: Accuracy on the test dataset without any attack.
-**Robust Accuracy**: Accuracy under adversarial attacks (e.g., PGD, FGSM).
-**Losses**: Both clean and adversarial losses.
---
## Dependencies
-**PyTorch**: For deep learning and model training.
-**torchvision**: For image processing and dataset utilities.
-**NumPy**: For numerical computations.
-**matplotlib**: For plotting training and evaluation results.
-**scikit-learn**: For t-SNE visualization.
---
## Author
-**Mina Moshfegh**
---
### Notes:
- This README provides clear setup instructions, usage details, configuration, and dependency installation steps.
