adversarial_robustness

Adversarial Robustness

This repository contains the code needed to evaluate models trained in Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples (Gowal et al., 2020) and in Fixing Data Augmentation to Improve Adversarial Robustness (Rebuffi et al., 2021).

Contents

We have released our top-performing models in two formats compatible with JAX and PyTorch. This repository also contains our model definitions.

Running the code

Downloading a model

Download a model from links listed in the following table. Clean and robust accuracies are measured on the full test set. The robust accuracy is measured using AutoAttack. The following table contains the models from Gowal et al., 2020.

dataset	norm	radius	architecture	extra data	clean	robust	link
CIFAR-10	ℓ∞	8 / 255	WRN-70-16	✓	91.10%	65.88%	jax, pt
CIFAR-10	ℓ∞	8 / 255	WRN-28-10	✓	89.48%	62.80%	jax, pt
CIFAR-10	ℓ∞	8 / 255	WRN-70-16	✗	85.29%	57.20%	jax, pt
CIFAR-10	ℓ∞	8 / 255	WRN-34-20	✗	85.64%	56.86%	jax, pt
CIFAR-10	ℓ2	128 / 255	WRN-70-16	✓	94.74%	80.53%	jax, pt
CIFAR-10	ℓ2	128 / 255	WRN-70-16	✗	90.90%	74.50%	jax, pt
CIFAR-100	ℓ∞	8 / 255	WRN-70-16	✓	69.15%	36.88%	jax, pt
CIFAR-100	ℓ∞	8 / 255	WRN-70-16	✗	60.86%	30.03%	jax, pt
MNIST	ℓ∞	0.3	WRN-28-10	✗	99.26%	96.34%	jax, pt

The following table contains the models from Rebuffi et al., 2021.

dataset	norm	radius	architecture	extra data	clean	robust	link
CIFAR-10	ℓ∞	8 / 255	WRN-106-16	✗	88.50%	64.64%	jax, pt
CIFAR-10	ℓ∞	8 / 255	WRN-70-16	✗	88.54%	64.25%	jax, pt
CIFAR-10	ℓ∞	8 / 255	WRN-28-10	✗	87.33%	60.75%	jax, pt
CIFAR-10	ℓ2	128 / 255	WRN-70-16	✗	92.41%	80.42%	jax, pt
CIFAR-10	ℓ2	128 / 255	WRN-28-10	✗	91.79%	78.80%	jax, pt
CIFAR-100	ℓ∞	8 / 255	WRN-70-16	✗	63.56%	34.64%	jax, pt
CIFAR-100	ℓ∞	8 / 255	WRN-28-10	✗	62.41%	32.06%	jax, pt

Installing

The following has been tested using Python 3.9.2. Using run.sh will create and activate a virtualenv, install all necessary dependencies and run a test program to ensure that you can import all the modules.

# Run from the parent directory.
sh adversarial_robustness/run.sh

To run the provided code, use this virtualenv:

source /tmp/adversarial_robustness_venv/bin/activate

You may want to edit requirements.txt before running run.sh if GPU support is needed (e.g., use jaxline==0.1.67+cuda111). See JAX's installation instructions for more details.

Using pre-trained models

Once downloaded, a model can be evaluated by running the eval.py script in either the jax or pytorch folders. E.g.:

cd jax
python3 eval.py \
  --ckpt=${PATH_TO_CHECKPOINT} --depth=70 --width=16 --dataset=cifar10

These models are also directly available within RobustBench's model zoo.

Training your own model

We also provide a training pipeline that reproduces results from both publications. This pipeline uses Jaxline and is written using JAX and Haiku. To train a model, modify the configuration in the get_config() function of jax/experiment.py and issue the following command from within the virtualenv created above:

cd jax
python3 train.py --config=experiment.py

The training pipeline can run with multiple worker machines and multiple devices (either GPU or TPU). See Jaxline for more details.

We do not provide a PyTorch implementation of our training pipeline. However, you may find one on GitHub, e.g., adversarial_robustness_pytorch (by Rahul Rade).

Datasets

Extracted dataset

Gowal et al. (2020) use samples extracted from TinyImages-80M. Unfortunately, since then, the official TinyImages-80M dataset has been withdrawn (due to the presence of offensive images). As such, we cannot provide a download link to our extrated data until we have manually verified that all extracted images are not offensive. If you want to reproduce our setup, consider the generated datasets below. We are also happy to help, so feel free to reach out to Sven Gowal directly.

Generated datasets

Rebuffi et al. (2021) use samples generated by a Denoising Diffusion Probabilistic Model (DDPM; Ho et al., 2020) to improve robustness. The DDPM is solely trained on the original training data and does not use additional external data. The following table links to datasets of 1M generated samples for CIFAR-10, CIFAR-100 and SVHN.

dataset	model	size	link
CIFAR-10	DDPM	1M	npz
CIFAR-100	DDPM	1M	npz
SVHN	DDPM	1M	npz

To load each dataset, use NumPy. E.g.:

npzfile = np.load('cifar10_ddpm.npz')
images = npzfile['image']
labels = npzfile['label']

Citing this work

If you use this code (or any derived code), data or these models in your work, please cite the relevant accompanying paper:

@article{gowal2020uncovering,
  title={Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples},
  author={Gowal, Sven and Qin, Chongli and Uesato, Jonathan and Mann, Timothy and Kohli, Pushmeet},
  journal={arXiv preprint arXiv:2010.03593},
  year={2020},
  url={https://arxiv.org/pdf/2010.03593}
}

and/or

@article{rebuffi2021fixing,
  title={Fixing Data Augmentation to Improve Adversarial Robustness},
  author={Rebuffi, Sylvestre-Alvise and Gowal, Sven and Calian, Dan A. and Stimberg, Florian and Wiles, Olivia and Mann, Timothy},
  journal={arXiv preprint arXiv:2103.01946},
  year={2021},
  url={https://arxiv.org/pdf/2103.01946}
}

Disclaimer

This is not an official Google product.