Skip to content

carlini/nn_robust_attacks

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.

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.

About

Robust evasion attacks against neural network to find adversarial examples

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages