Hard Label Black Box Node Injection Attack on Graph Neural Network

This is the official Pytorch code repository of the paper "Hard Label Black Box Node Injection Attack on Graph Neural Network"

Detailed code descriptions can be found in the separate subfolders

This code was tested with python 3.6 and pytorch 1.4

General Instructions adapted from CCS21_GNNattack

Train target models

To train target GNN models (GIN, SAG, GUNet) on three datasets (COIL-DEL, IMDB-BINARY, NCI1), use main.py

e.g., to train GIN model on NCI1 dataset, use:

python -u main.py --dataset NCI1 --model GIN

the trained model will be saved in path: ./trained_model (create by yourself if does not exist).

Attack target GNN

To attack target GNN models via our hard lable black-box adversarial attack, use test.py

e.g., to attack SAG model on IMDB-BINARY dataset (make sure you have trained the model before attack), use:

python -u test.py --dataset IMDB-BINARY --model SAG

the attack results will be saved in path: ./out1

Note: the meanings of some paras are:

--effective : whether or not use QEGC algorithm

--id : the id of this trail (10 trails in total)

--search : the search strategy of CGS algorithm

process the attack results

To process the attack results, use process_results.py It will print the SR/AP/AQ/QT/APR(ignore)/AD(ignore) of one trail.

e.g., run:

python -u process_results.py --dataset IMDB-BINARY --id 5 --effective 0

it will print the SR/AP/AQ/AT of our and random attacks against GIN model which is trained on IMDB-BINARY dataset.

Note: the attack results of two GNN models (e.g., GIN and SAG) will overlap so make sure you save them in different folders.

Random attack

To attack target GNN models via random attack, use random_attack.py

e.g., to attack GUNet model on IMDB-BINARY dataset (make sure you have trained the model before attack), use:

python -u random_attack.py --dataset IMDB-BINARY --model GUN

the attack results will be saved in path: ./out1

RL-S2V attack

please refer the initial paper and code of RL-S2V

Detect Adversarial Graphs

The first countmeasure against our attack: detection.

generate training dataset for detectors

To generate training dataset for binary detectors, use detection_train_dataset.py. It will randomly select some graphs in the original training dataset and acquire their corresponding adversarial graphs via our attack.

e.g., to get training dateset for GIN detector (i.e., deploy GIN as the binary detector) on COIL-DEL dataset, run:

python -u detection_train_dataset.py --dataset COIL-DEL

the generated training dataset will be saved in folder: ./detection (create by yourself if it does not exist)

Note: the testing dataset is the adversarial graphs and there corresponding normal graphs in [## Attack target GNN]

To generate training dataset with PGD attack, please refer to the initial paper and code of PGD

train and test detectors

To train detectors and to evaluate their effectiveness, use binary_detect.py e.g., to train a GIN detector with training dataset generated by our attack and to evaluate its performance, run:

python -u binary_detect.py --method Our --dataset COIL-DEL --model GIN

Note: --method : the attack method to generate training dataset for the detector. ['Our' or 'Typo']

--model : the model structure of detector