MagNet - PyTorch Implementation

PyTorch implementation of MagNet: a Two-Pronged Defense against Adversarial Examples

Paper: https://arxiv.org/pdf/1705.09064.pdf

Steps

1. Attack Models: Trained classifier models for the datasets MNIST, Fashion-MNIST & CIFAR-10 are availabe in the models directory. If you want to train your own classifier define them in classifiers.py and train them using train_classifier.py

2. Defensive Models: Trained autoencoder models for the datasets MNIST, Fashion-MNIST & CIFAR-10 are availabe in the models directory. If you want to train your own autoencoder define them in defensive_models.py and train them using train_defensive_model.py

3. Adversarial Examples: generate_adversarial_examples.py will generated the adversarial images using common adversarial attacks using Foolbox.

4. Evaluation: Performance of a defensive model against various attacks can be evalauted using evaluate_defensive_model.py. Check the summary csv files for each dataset inside the results' directory