unsupervised_adversarial_training

Unsupervised Adversarial Training (UAT)

This repository contains the trained model and dataset used for Unsupervised Adversarial Training (UAT) from the paper Are Labels Required for Improving Adversarial Robustness?

Contents

This repo serves two primary functions:

• Data release: We share indices for the 80 Million Tiny Images Dataset subset used in our experiments, and a utility for loading the data.
• Model release: We have released our top-performing model on TF-Hub, and include an example demonstrating how to use it.

Running the code

Using the model

Our model is available via TF-Hub. For example usage, refer to quick_eval_cifar.py. The preferred method of running this script is through run.sh, which will set up a virtual environment, install the dependendencies, and run the evaluation script, which will print the adversarial accuracy of the model.

cd /path/to/deepmind_research
unsupervised_adversarial_training/run.sh

Viewing the dataset

First, download the 80 Million Tiny Images Dataset image binary from the official web page: http://horatio.cs.nyu.edu/mit/tiny/data/index.html

Note this file is very large, and requires 227 GB of disc space.

The file tiny_200K_idxs.txt indicates which images from the dataset form the 80M@200K training set used in the paper. For example usage, refer to save_example_images.py.

To view example images from this dataset, use the command:

cd /path/to/deepmind_research
python -m unsupervised_adversarial_training.save_example_images \
  --data_bin_path=/path/to/tiny_images.bin

This will save the first 100 images to the directory unsupervised_adversarial_training/images.

Citing this work

If you use this code in your work, please cite the accompanying paper:

@inproceedings{uat2019,
  title={Are Labels Required for Improving Adversarial Robustness?},
  author={Jonathan Uesato and Jean-Baptiste Alayrac and Po-Sen Huang and
  Robert Stanforth and Alhussein Fawzi and Pushmeet Kohli},
  booktitle={Advances in Neural Information Processing Systems},
  year={2019}
}

Disclaimer

This is not an official Google product.