Knowledge-Enriched-Distributional-Model-Inversion-Attacks

This is a PyTorch implementation of our paper at ICCV2021:

Knowledge Enriched Distributional Model Inversion Attacks [paper] [arxiv]

We propose a novel 'Inversion-Specific GAN' that can better distill knowledge useful for performing attacks on private models from public data. Moreover, we propose to model a private data distribution for each target class which refers to 'Distributional Recovery'.

Requirement

This code has been tested with Python 3.6, PyTorch 1.0 and cuda 10.0.

Getting Started

• Install required packages.
• Download relevant datasets including Celeba, MNIST, CIFAR10.
• Get target model prepared or run our code python train_classifier.py
  Note that this code only provides three model architectures: VGG16, IR152, Facenet. And pretrained checkpoints for the three models can be downloaded at https://drive.google.com/drive/folders/1U4gekn72UX_n1pHdm9GQUQwwYVDvpTfN?usp=sharing.

Build a inversion-specific GAN

• Modify the configuration in 'celeba.json'.
• Modify the target model path in 'k+1_gan.py' to your customized path.
• Run python k+1_gan.py.
• Model checkpoints and generated image results are saved in folder ’improvedGAN‘.
• A general GAN can be obtained as a baseline by running python binary_gan.py.
• Pretrained binary GAN and inversion-specific GAN can be downloaded at https://drive.google.com/drive/folders/1L3frX-CE4j36pe5vVWuy3SgKGS9kkA70?usp=sharing.

Distributional Recovery

Run python recovery.py

• --model chooses the target model to attack.
• --improved_flag indicates if an inversion-specfic GAN is used. If False, then a general GAN will be applied.
• --dist_flag indicates if distributional recovery is performed. If False, then optimization is simply applied on a single sample instead of a distribution.
• By setting both improved_flag and dist_flag be False, we are simply using the method proposed in [1].

Reference

[1] Zhang, Yuheng, et al. "The secret revealer: Generative model-inversion attacks against deep neural networks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.