Ensemble Adversarial Training on MNIST
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models Initial commit May 30, 2017
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carlini.py clearer variable name Jun 20, 2017
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README.md

Ensemble Adversarial Training

This repository contains code to reproduce results from the paper:

Ensemble Adversarial Training: Attacks and Defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh and Patrick McDaniel
ArXiv report: https://arxiv.org/abs/1705.07204


REQUIREMENTS

The code was tested with Python 2.7.12, Tensorflow 1.0.1 and Keras 1.2.2.

EXPERIMENTS

We start by training a few simple MNIST models. These are described in mnist.py.

python -m train models/modelA --type=0
python -m train models/modelB --type=1
python -m train models/modelC --type=2
python -m train models/modelD --type=3

Then, we can use (standard) Adversarial Training or Ensemble Adversarial Training (we train for either 6 or 12 epochs in the paper). With Ensemble Adversarial Training, we additionally augment the training data with adversarial examples crafted from external pre-trained models (models A, C and D here):

python -m train_adv models/modelA_adv --type=0 --epochs=12
python -m train_adv models/modelA_ens models/modelA models/modelC models/modelD --type=0 --epochs=12

The accuracy of the models on the MNIST test set can be computed using

python -m simple_eval test [model(s)]

To evaluate robustness to various attacks, we use

python -m simple_eval [attack] [source_model] [target_model(s)] [--parameters (opt)]

The attack can be:

Attack Description Parameters
fgs Standard FGSM eps (the norm of the perturbation)
rand_fgs Our FGSM variant that prepends the gradient computation by a random step eps (the norm of the total perturbation); alpha (the norm of the random perturbation)
ifgs The iterative FGSM eps (the norm of the perturbation); steps (the number of iterative FGSM steps)
CW The Carlini and Wagner attack eps (the norm of the perturbation); kappa (attack confidence)

Note that due to GPU non-determinism, the obtained results may vary by a few percent compared to those reported in the paper. Nevertheless, we consistently observe the following:

  • Standard Adversarial Training performs worse on transferred FGSM examples than on a "direct" FGSM attack on the model due to a gradient masking effect.
  • Our RAND+FGSM attack outperforms the FGSM when applied to any model. The gap is particularly pronounced for the adversarially trained model.
  • Ensemble Adversarial Training is more robust than (standard) adversarial training to transferred examples computed using any of the attacks above.
CONTACT

Questions and suggestions can be sent to tramer@cs.stanford.edu