RuntianZ / free_adv_train

Free Adversarial Training

This repository belongs to the Free Adversarial Training paper. The implementation is inspired by CIFAR10 Adversarial Example Challenge so to them we give the credit.

Demo

To train a new robust model for free! run the following command specifying the replay parameter m:

python free_train.py -m 8

To evaluate a robust model using PGD-20 with 2 random restarts run:

python multi_restart_pgd_attack.py --model_dir $MODEL_DIR --num_restarts 2

Note that if you have trained a CIFAR-100 model, even for evaluation, you should pass the dataset argument. For example:

python multi_restart_pgd_attack.py --model_dir $MODEL_DIR_TO_CIFAR100 --num_restarts 2 -d cifar100

Requirements

To install all the requirements plus tensorflow for multi-gpus run: (Inspired By Illarion ikhlestov )

pip install -r requirements/gpu.txt

Alternatively, to install the requirements plus tensorflow for cpu run:

pip install -r requirements/cpu.txt

To prepare the data, please see Datasets section

If you find the paper or the code useful for your study, please consider citing the free training paper:

@ARTICLE{2019arXiv190412843S,
   author = {{Shafahi}, A. and {Najibi}, M. and {Ghiasi}, A. and {Xu}, Z. and 
	{Dickerson}, J. and {Studer}, C. and {Davis}, L. and {Taylor}, G. and {Goldstein}, T.},
    title = "{Adversarial Training for Free!}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1904.12843},
 primaryClass = "cs.LG",
 keywords = {Computer Science - Learning, Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning},
     year = 2019,
    month = apr,
   adsurl = {http://adsabs.harvard.edu/abs/2019arXiv190412843S},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}