ResNet ImageNet Code

This repository provides code for both training and using the restricted robust resnet models from the paper:

There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)
Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry
https://arxiv.org/abs/1805.12152

We show that adversarial robustness often inevitablely results in accuracy loss. The silver lining: adversarial training induces more semantically meaningful gradients and gives adversarial examples with GAN-like trajectories:

General overview

Before trying to run anything:

• Run ./setup.sh.
• Get a downloaded version of the ImageNet training set. By default the code looks for this directory in the environmental variable INET_DIR. For example, run export INET_DIR=/scratch/datasets/imagenet/.

The code is organized so that you can:

• Train your own robust restricted ImageNet models (via ez_train.sh)
• Produce adversarial examples and visualize gradients, with example code in example_use.ipynb
• Reproduce the ImageNet examples seen in the paper (via make_viz.sh).

Pretrained models

This repository comes with (after following the instructions) three restricted ImageNet pretrained models:

• data/train_224_nat_slim corresponds to a naturally trained model
• data/train_224_robust_eps_0.005_lp_inf_slim corresponds to an linf adv trained model with eps 0.005
• data/train_224_robust_eps_1.0_lp_2_slim corresponds to an l2 adv trained model with eps 1.0

You will need to set the model ckpt directory in the various scripts/ipynb files where appropriate if you want to complete any nontrivial tasks.