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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. This repo is for the CIFAR-10 and CIFAR-100 datasets and is in Tensorflow. Our Free-m models can acheive comparable performance with conventional PGD adversarial training at a fraction of the time.

News!: We have released our ImageNet implementation of Free adversarial training in Pytorch !

CIFAR-10 WRN 32-10 (L-inf epsilon=8):
Model Natural PGD-100 CW-100 10 restart PGD-20 train-time (min)
Natrual 95.01 0.00 0.00 0.00 780
Free-2 91.45 33.20 34.57 33.41 816
Free-4 87.83 40.35 41.96 40.73 800
Free-8 85.96 46.19 46.60 46.33 785
Free-10 83.94 45.79 45.86 45.94 785
Madry 7-PGD (public model) 87.25 45.29 46.52 45.53 5418
CIFAR-100 WRN 32-10 (L-inf epsilon=8):
Model Natural PGD-20 PGD-100 train-time (min)
Natrual 78.84 0.00 0.00 811
Free-2 69.20 15.37 14.86 816
Free-4 65.28 20.64 20.15 767
Free-8 62.13 25.88 25.58 780
Free-10 59.27 25.15 24.88 776
Madry 2-PGD trained 67.94 17.08 16.50 2053
Madry 7-PGD trained 59.87 22.76 22.52 5157


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

python -m 8

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

python --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 --model_dir $MODEL_DIR_TO_CIFAR100 --num_restarts 2 -d cifar100


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:

  title={Adversarial Training for Free!},
  author={Shafahi, Ali and Najibi, Mahyar and Ghiasi, Amin and Xu, Zheng and Dickerson, John and Studer, Christoph and Davis, Larry S and Taylor, Gavin and Goldstein, Tom},
  journal={arXiv preprint arXiv:1904.12843},

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