nn_robust_attacks

by carlini

Robust evasion attacks against neural network to find adversarial examples

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About

Corresponding code to the paper "Towards Evaluating the Robustness of Neural Networks" by Nicholas Carlini and David Wagner, at IEEE Symposium on Security & Privacy, 2017.

Implementations of the three attack algorithms in Tensorflow. It runs correctly on Python 3 (and probably Python 2 without many changes).

To evaluate the robustness of a neural network, create a model class with a predict method that will run the prediction network without softmax. The model should have variables

model.image_size: size of the image (e.g., 28 for MNIST, 32 for CIFAR)
model.num_channels: 1 for greyscale, 3 for color images
model.num_labels: total number of valid labels (e.g., 10 for MNIST/CIFAR)

Running attacks

     from robust_attacks import CarliniL2
     CarliniL2(sess, model).attack(inputs, targets)

where inputs are a (batch x height x width x channels) tensor and targets are a (batch x classes) tensor. The L2 attack supports a batch_size paramater to run attacks in parallel. Each attack has many tunable hyper-paramaters. All are intuitive and strictly increase attack efficacy in one direction and are more efficient in the other direction.

Pre-requisites

The following steps should be sufficient to get these attacks up and running on most Linux-based systems.

    sudo apt-get install python3-pip
    sudo pip3 install --upgrade pip
    sudo pip3 install pillow scipy numpy tensorflow-gpu keras h5py

To create the MNIST/CIFAR models:

python3 train_models.py

To download the inception model:

python3 setup_inception.py

And finally to test the attacks

python3 test_attack.py

This code is provided under the BSD 2-Clause, Copyright 2016 to Nicholas Carlini.

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