by carlini

Robust evasion attacks against neural network to find adversarial examples

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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.


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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