mirror's Introduction

Official Code for MIRROR (NDSS 2022)

This is the PyTorch implementation for NDSS 2022 paper "MIRROR: Model Inversion for Deep Learning Network with High Fidelity". For more inversion results, please refer to mirror github page.

Note: we only uploaded the scripts and cache files for resnet50 and vgg16 model. Others will be updated soon.

Environment

conda env create -f environment.yml
conda activate mirror

Usage

1. Download cache files

python my_download_cache.py

2. White-box invert ResNet50

Conduct the inversion.

zsh scripts/run_vggface2_resnet50_whitebox.sh

Test the inversion.

zsh scripts/run_vggface2_resnet50_whitebox_test.sh

3. Black-box invert ResNet50

Conduct the inversion.

zsh scripts/run_vggface2_resnet50_blackbox.sh

Test the inversion.

zsh scripts/run_vggface2_resnet50_blackbox_test.sh

Build cache files

We need to first generate styelgan's images if we haven't done it.

python my_sample_z_w_space.py

Use inception_resnetv1_vggface2 as an example. We first generate the outputs of the network on the stylegan's samples. Then we merge them into one file.

python my_generate_blackbox_attack_dataset.py --arch_name inception_resnetv1_vggface2 stylegan
python my_merge_all_tensors.py blackbox_attack_data/stylegan/inception_resnetv1_vggface2/no_dropout --remove

Acknowledgement

The StyleGAN models are based on genforce/genforce.

VGG16/VGG16BN/Resnet50 models are from their official websites.

InceptionResnetV1 is from timesler/facenet-pytorch.

SphereFace is from clcarwin/sphereface_pytorch.

BibTex

Please cite our work as follows for any purpose of usage.

@inproceedings{An.Mirror.NDSS.2022,
    title={MIRROR: Model Inversion for Deep Learning Network with High Fidelity},
    author={An, Shengwei and Tao, Guanhong and Xu, Qiuling and Liu, Yingqi and Shen, Guangyu and Yao, Yuan and Xu, Jingwei and Zhang, Xiangyu},
    booktitle={Proceedings of the Network and Distributed Systems Security Symposium (NDSS 2022)},
    year={2022}
}

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