This is a code implementation for model inversion attack, both in white-box and black-box settings.
Black-box attack implementation. reference: https://github.com/yziqi/adversarial-model-inversion/tree/master
Complete the black-box attack implementation. Please find the folder blackbox, and run python train_inversion.py --save-model --epochs 50 --lr 0.0005 --log-interval 100.
Complete the white-box attack implementation. Please find the folder whitebox, and run python train_inversion.py --num_epoch 200
Up to now, we assume that we have a generic public dataset to train our GAN and inversion model.
TODO: Update the model inversion attack on CIFAR10, CIFAR100 datasets
Due to an illness, I will complete this task later.
Complete white-box model inversion attack for CIFAR10 and CIAFAR100 datasets.
In the \blackbox folder, you can find the main code ideal.py for FedMD and FedED on cifar10 and cifar100 datasets. Just refer the run.sh for the usage. And metric.py is used to compute the FID score. Also, you need the targeted model for different methods, which are placed in the \pretrained folder. The model required can be downloaded here: https://drive.google.com/drive/folders/1Cqkz4aGR5orTFgbJeVHgEOyS4ToEXe2-?usp=share_link
In the \whitebox folder, you can find cifar10 and cifar100 folders. Each folder contains code for different methods. Just refer the run.sh for the usage. And metric.py is used to compute the FID score. Also, you need the targeted model for different methods, which are placed in the \result_model folder. The model required can be downloaded here:
cifar10: https://drive.google.com/drive/folders/1ZqEcc4I5vVTzvSN2xi1IXrF5vTNZht3z?usp=share_link
cifar100: https://drive.google.com/drive/folders/1zPqEt4pp7fL4yGrFnmAGzIlSsaDYZJ2L?usp=sharing
whitebox attack is highly referenced from https://github.com/MKariya1998/GMI-Attack
blackbox attack is high referenced from https://github.com/SonyResearch/IDEAL
Fix some errors in the code \whitebox\cifar100\train_inversion.py, mainly about the random noise inputs and generator. Now you can find the new code in \whitebox\cifar100\train_inversion.py.
Now you can refer to \whitebox\cifar100\train_inversion.py to directly obtain the FID score.