README.md

An Approximate Memory based Defense against Model Inversion Attacks to Neural Networks

This repo provides an example of defending against model inversion attacks in the paper An Approximate Memory based Defense against Model Inversion Attacks to Neural Networks and MIDAS: Model Inversion Defenses Using an Approximate Memory System

Shallow Neural Networks

Following the work of "Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures", the classifier is trained over AT&T dataset.

Quick Start

Run the code in MIDAS_shallow.ipynb

Deep Neural Networks

Following the work of "Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment", the classifier is trained over the Facescrub dataset. Download 150x150 aligned images from FaceScrub dataset and save them in data/. The inversion model is trained over public information, the CelebA dataset. Download the CelebA dataset to data/ and crop the images by crop.ipynb. Please download the trained classifier and inversion model and save them in results/deep/.

Dependencies

The code is written in Python3. You can install the required packages by running:

$ pip3 install -r requirements.txt

Quick Start

Run the code in MIDAS_deep.ipynb

Citation

@article{xu2022approximate,
  title={An Approximate Memory based Defense against Model Inversion Attacks to Neural Networks},
  author={Xu, Qian and Arafin, Md Tanvir and Qu, Gang},
  journal={IEEE Transactions on Emerging Topics in Computing},
  year={2022},
  publisher={IEEE}
}

@inproceedings{xu2020midas,
  title={MIDAS: Model Inversion Defenses Using an Approximate Memory System},
  author={Xu, Qian and Arafin, Md Tanvir and Qu, Gang},
  booktitle={2020 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)},
  pages={1--4},
  year={2020},
  organization={IEEE}
}