PyGIP

PyGIP is a Python library designed for experimenting with graph-based model extraction attacks and defenses. It provides a modular framework to implement and test attack and defense strategies on graph datasets.

Installation

To get started with PyGIP, set up your environment by installing the required dependencies:

pip install -r reqs.txt

Ensure you have Python installed (version 3.8 or higher recommended) along with the necessary libraries listed in reqs.txt.

Specifically, using following command to install dgl 2.2.1 and ensure your pytorch==2.3.0.

pip install dgl==2.2.1 -f https://data.dgl.ai/wheels/torch-2.3/repo.html

cuda

pip install dgl==2.2.1 -f https://data.dgl.ai/wheels/torch-2.3/cu118/repo.html

Quick Start

Here’s a simple example to launch a Model Extraction Attack using PyGIP:

from datasets import Cora
from models.attack import ModelExtractionAttack0

# Load the Cora dataset
dataset = Cora()

# Initialize the attack with a sampling ratio of 0.25
mea = ModelExtractionAttack0(dataset, 0.25)

# Execute the attack
mea.attack()

This code loads the Cora dataset, initializes a basic model extraction attack (ModelExtractionAttack0), and runs the attack with a specified sampling ratio.

And a simple example to run a Defense method against Model Extraction Attack:

from datasets import Cora
from models.defense import RandomWM

# Load the Cora dataset
dataset = Cora()

# Initialize the attack with a sampling ratio of 0.25
mead = RandomWM(dataset, 0.25)

# Execute the attack
mead.defend()

which runs the Random Watermarking Graph to defend against MEA.

If you want to use cuda, please set environment variable:

export PYGIP_DEVICE=cuda:0

Implementation & Contributors Guideline

Refer to Implementation Guideline

Refer to Contributors Guideline

License

MIT License

Contact

For questions or contributions, please contact blshen@fsu.edu.