Property Inference Attacks on Convolutional Neural Networks:Influence and Implications of Target Model’s Complexity

Authors: Mathias Parisot, Balazs Pejo, and Dayana Spagnuelo

This repository is the official implementation of Property Inference Attacks on Convolutional Neural Networks:Influence and Implications of Target Model’s Complexity.

Requirements

This repository was tested using Python 3 only, please make sure not to use Python 2. To install the dependencies:

pip install -r requirements.txt

Downloading CelebA

In case you want to train and evaluate the shadow models, you will also need to download CelebA. For some reasons, downloading CelebA via Pytorch didn't work, so here is how to download CelebA manualy:

• on the CelebA webpage, got to their Google drive directory. The disposition of the directory is as follow:

Img/
Anno/
Eval/
README.txt

• go to Img/ and download the zip file img_align_celeba.zip
• go to Ano/ and download all the .txt files, there should be 5
• go to Eval/ and doanload list_eval_partition.txt
• Move all the downloaded files into data/celeba and extract img_align_celeba.zip. After that, data/celeba should look as follow:

img_align_celeba/
identity_CelebA.txt
img_align_celeba.zip
list_attr_celeba.txt
list_bbox_celeba.txt
list_eval_partition.txt
list_landmarks_align_celeba.txt
list_landmarks_celeba.txt

Once this is done, you can follow the next sections to train and test the shadow and attack models.

Training

To train the shadow models (architecture 1, 2, 3, and 4 from the paper), run this command:

python train_shadow.py --models a1 a2 a3 a4 --csv template_output.csv 

To train the attack models, run this command:

python train_attack.py --csv template_output.csv 

For more specific parameters, please check the command line arguments of the respective files.

Evaluation

After training the shadow models, you can evaluate their performance on CelebA by running:

python test_shadow.py --shadow_csv template_output.csv

After training the attack models, you can evaluate their performance on the shadow model test dataset by running:

python test_attack.py --csv template_output.csv