MIAA

code of "Improving Membership Inference Attack Robustness With Attention Against Adversarial Examples"

Code modification based on "MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples" code

Required python tool:

Dataset description

python main_run.py

result saved at log/result.log

If you use this code, please cite the following paper:

We would like to thank Jinyuan Jia and Neil Zhenqiang Gong (Duke University) for their open source code of MemGuard.

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
data/location		data/location
model		model
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
config.ini		config.ini
cuda-repo-ubuntu1604_9.0.176-1_amd64.deb		cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
defense_None_framework.py		defense_None_framework.py
defense_framework.py		defense_framework.py
evaluate_nn_attack.py		evaluate_nn_attack.py
evaluate_our_attack.py		evaluate_our_attack.py
evaluate_rf_attack.py		evaluate_rf_attack.py
input_data_class.py		input_data_class.py
log.ini		log.ini
main_run.py		main_run.py
makeAudit.ipynb		makeAudit.ipynb
makeCHMnist.ipynb		makeCHMnist.ipynb
makeCifar10.ipynb		makeCifar10.ipynb
makeMNIST.ipynb		makeMNIST.ipynb
makeYaleFace.ipynb		makeYaleFace.ipynb
paper_draw.ipynb		paper_draw.ipynb
paper_draw_Interpretability.ipynb		paper_draw_Interpretability.ipynb
run_location_defense.py		run_location_defense.py
train_attack_shadow_model.py		train_attack_shadow_model.py
train_defense_model_defensemodel.py		train_defense_model_defensemodel.py
train_user_classification_model.py		train_user_classification_model.py