Robust evasion attacks against neural network to find adversarial examples
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)
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
This code is provided under the BSD 2-Clause, Copyright 2016 to Nicholas Carlini.