NeuGuard

We use jupyter notebook to demo the codes and show the attack results on our pre-trained models.

Here are three separate jupyter files for different datasets.

• NeuGuard_ALEXNET_cifar10.ipynb for CIFAR10 dataset
• NeuGuard_ALEXNET_cifar100.ipynb for CIFAR100 dataset
• NeuGuard_Texas100.ipynb for Texas100 dataset.

Requirements:

CUDA Enabled GPU hardware

• The code should be able to run in most pytorch versions.

• You can create environment with following code:

conda create -n pytorch pytorch-gpu torchvision cudatoolkit

conda activate pytorch

conda install -c conda-forge progress

conda install -c conda-forge matplotlib

pip install notebook

pip install scikit-metrics

After the first time installation, you can use conda activate pytorch when you need to use the installed environment.

• We also provide the pytorch.yml file as a demo of the running environment.

You can create the environment using the following command.

conda env create -f pytorch.yml

Detailed instructions please check the conda website.

We specify the running environment here, but other versions of python and pytorch might also work.

Datasets

To run the attack evaluation, you will need to download the three datasets first.

For the Texas100 dataset, the default way to load the data takes long time, we save a npz file to speed up the loading. We provide both ways to load the data.

• Name: texas100_data.npz download here.
• We load Texas100 data with a randomized order following the file random_r_texas100_prune. Please download it before running the code. Using python 2 requires changes to the code that loads this file, and we provide the corresponding code for replacement.

For CIFAR10 and CIFAR100 datasets, they will download automatically if you don't have them.

Run the code

For the convenience of running the experiments, we provide the code in the jupyter notebook.

Once you open the jupyter file, you can search for the corresponding cell using the # keywords given in each step and run the cells following the instructions.

For each code in the jupyter notebook, we have similar running steps.

1. Run all the cells above the # start train cell.
2. Load model in the # load saved model cell and the following two cells to check the model accuracy.
3. Load unsort NSH attack model in the # load membership inference attack cell and the following five cells to check the model accuracy.
4. Load sort NN attack model in the # load NN attack model cell and the following two cells to check the model accuracy.
5. Run the cell start in # load for metric base attack and the following cells to perform the metric based attack.
6. Run the cell start in # load for c&w label-only attack and the following cells to perform the c&w label-only attack.

Pretrained models

We provide pretrianed models used for the work. Specifically, we upload the models trained using the proposed NeuGuard method for all three datasets, and we include both sorted and unsorted NN based attack models, respectively.

The pretrained models can be downloaded here.