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Robust evasion attacks against neural network to find adversarial examples
| Date | Stars |
|---|---|
| 2026-07-31 | 864 |
| 2026-08-01 | 864 |
| 2026-08-02 | 864 |
| 2026-08-06 | 864 |
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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
```python
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.
```bash
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:
```bash
python3 train_models.py
```
#### To download the inception model:
```bash
python3 setup_inception.py
```
#### And finally to test the attacks
```bash
python3 test_attack.py
```
This code is provided under the BSD 2-Clause, Copyright 2016 to Nicholas Carlini.
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Nicholas Carlini
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Anshuman Suri · DatologyAI · United States
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:69e3a23de14f0f55, llm:Description: 'Robust evasion attacks against neural network to find adversarial examples' (repo: carlini/nn_robust_attacks)
matched fp:69e3a23de14f0f55, llm:Description: 'Robust evasion attacks against neural network to find adversarial examples' (repo: carlini/nn_robust_attacks)