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 example 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

Using the model

Once downloaded, a model can be evaluated (clean accuracy) 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

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, 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}
}

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.