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)

Run the attacks with

    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.

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 GPLv3, Copyright 2016 to Nicholas Carlini.