Awesome

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

Pouya Samangouei*, Maya Kabkab*, Rama Chellappa

[*: authors contributed equally]

This repository contains the implementation of our ICLR-18 paper: Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

If you find this code or the paper useful, please consider citing:

@inproceedings{defensegan,
  title={Defense-GAN: Protecting classifiers against adversarial attacks using generative models},
  author={Samangouei, Pouya and Kabkab, Maya and Chellappa, Rama},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

Contents

1. Installation
2. Usage
   • Train a GAN model
   • Black-box attacks
   • White-box attacks

Installation

1. Clone this repository:

git clone --recursive https://github.com/kabkabm/defensegan
cd defensegan
git submodule update --init --recursive

2. Install requirements:

pip install -r requirements.txt

Note: if you don't have a GPU install the cpu version of TensorFlow 1.7.

3. Download the dataset and prepare data directory:

python download_dataset.py [mnist|f-mnist|celeba]

4. Create or link output and debug directories:

mkdir output
mkdir debug

or

ln -s <path-to-output> output
ln -s <path-to-debug> debug

Usage

Train a GAN model

python train.py --cfg <path> --is_train <extra-args>

• --cfg This can be set to either a .yml configuration file like the ones in experiments/cfgs, or an output directory path.
• <extra-args> can be any parameter that is defined in the config file.

The training will create a directory in the output directory per experiment with the same name as <cfgs> to save the model checkpoints. If <extra-args> are different from the ones that are defined in <config>, the output directory name will reflect the difference.

A config file is saved into each experiment directory so that they can be loaded if <path> is the address to that directory.

Example

After running

python train.py --cfg experiments/cfgs/gans/mnist.yml --is_train

output/gans/mnist will be created.

[optional] Save reconstructions and datasets into cache:

python train.py --cfg experiments/cfgs/<config> --save_recs
python train.py --cfg experiments/cfgs/<config> --save_ds

Example

After running the training code for mnist, the reconstructions and the dataset can be saved with:

python train.py --cfg output/gans/mnist --save_recs
python train.py --cfg output/gans/mnist --save_ds

As training goes on, sample outputs of the generator are written to debug/gans/<model_config>.

Black-box attacks

To perform black-box experiments run blackbox.py [Table 1 and 2 of the paper]:

python blackbox.py --cfg <path> \
    --results_dir <results_path> \
    --bb_model {A, B, C, D, E} \
    --sub_model {A, B, C, D, E} \
    --fgsm_eps <epsilon> \
    --defense_type {none|defense_gan|adv_tr}
    [--train_on_recs or --online_training]
    <optional-arguments>

• --cfg is the path to the config file for training the iWGAN. This can also be the path to the output directory of the model.

• --results_dir The path where the final results are saved in text files.

• --bb_model The black-box model architectures that are used in Table 1 and Table 2.

• --sub_model The substitute model architectures that are used in Table 1 and Table 2.

• --defense_type specifies the type of defense to protect the classifier.

• --train_on_recs or --online_training These parameters are optional. If they are set, the classifier will be trained on the reconstructions of Defense-GAN (e.g. in column Defense-GAN-Rec of Table 1 and 2). Otherwise, the results are for Defense-GAN-Orig. Note --online_training will take a while if --rec_iters, or L in the paper, is set to a large value.

• <optional-arguments> A list of --<arg_name> <arg_val> that are the same as the hyperparemeters that are defined in config files (all lower case), and also a list of flags in blackbox.py. The most important ones are:

  • --rec_iters The number of GD reconstruction iterations for Defense-GAN, or L in the paper.
  • --rec_lr The learning rate of the reconstruction step.
  • --rec_rr The number of random restarts for the reconstruction step, or R in the paper.
  • --num_train The number of images to train the black-box model on. For debugging purposes set this to a small value.
  • --num_test The number of images to test on. For debugging purposes set this to a small value.
  • --debug This will save qualitative attack and reconstruction results in debug directory and will not run the adversarial attack part of the code.

• Refer to blackbox.py for more flag descriptions.

Example

• Row 1 of Table 1 Defense-GAN-Orig:

python blackbox.py --cfg output/gans/mnist \
    --results_dir defensegan \
    --bb_model A \
    --sub_model B \
    --fgsm_eps 0.3 \
    --defense_type defense_gan

• If you set --nb_epochs 1 --nb_epochs_s 1 --data_aug 1 you will get a quick glance of how the script works.

White-box attacks

To test Defense-GAN for white-box attacks run whitebox.py [Tables 4, 5, 12 of the paper]:

python whitebox.py --cfg <path> \
       --results_dir <results-dir> \
       --attack_type {fgsm, rand_fgsm, cw} \
       --defense_type {none|defense_gan|adv_tr} \
       --model {A, B, C, D} \
       [--train_on_recs or --online_training]
       <optional-arguments>

• --cfg is the path to the config file for training the iWGAN. This can also be the path to the output directory of the model.
• --results_dir The path where the final results are saved in text files.
• --defense_type specifies the type of defense to protect the classifier.
• --train_on_recs or --online_training These parameters are optional. If they are set, the classifier will be trained on the reconstructions of Defense-GAN (e.g. in column Defense-GAN-Rec of Table 1 and 2). Otherwise, the results are for Defense-GAN-Orig. Note --online_training will take a while if --rec_iters, or L in the paper, is set to a large value.
• <optional-arguments> A list of --<arg_name> <arg_val> that are the same as the hyperparemeters that are defined in config files (all lower case), and also a list of flags in whitebox.py. The most important ones are:
  • --rec_iters The number of GD reconstruction iterations for Defense-GAN, or L in the paper.
  • --rec_lr The learning rate of the reconstruction step.
  • --rec_rr The number of random restarts for the reconstruction step, or R in the paper.
  • --num_test The number of images to test on. For debugging purposes set this to a small value.
• Refer to whitebox.py for more flag descriptions.

Example

First row of Table 4:

python whitebox.py --cfg <path> \
       --results_dir whitebox \
       --attack_type fgsm \
       --defense_type defense_gan \
       --model A

• If you want to quickly see how the scripts work, add the following flags:

--nb_epochs 1 --num_tests 400