This repo provides the implementation of provable robustness against backdoor attacks.
The code requires Python >=3.6. The required packages can be installed by:
pip install -r requirements.txt
Note that PyTorch may need to be installed manually because of different platforms and CUDA drivers.
The MNIST and CIFAR-10 datasets will be downloaded at running time. The spam classification dataset can be downloaded here and put the spambase.data file under the raw_data/ folder. For the ImageNet dataset, the dog vs. cat dataset can be downloaded here, and one can extract the train.zip file under the raw_data/dog_and_cat/ folder. We processed the dog vs. fish dataset by ourselves and have not open-sourced it yet.
Train deep neural networks with smoothing noise scale 1.0 on a Trojaned CIFAR-10 dataset, in which 2% data cases are poisoned using four-pixel patterns with perturbation norm 0.1:
python train.py --sigma 1.0 --dataset cifar --atk_method fourpixel --poison_r 0.02 --delta 0.1
Evaluate:
python eval.py --sigma 1.0 --dataset cifar --atk_method fourpixel --poison_r 0.02 --delta 0.1
Train differentially private deep neural networks with smoothing noise scale 2.0, where gradient clip norm is 5.0 and gradient noise scale is 4.0, on a Trojaned MNIST dataset in which 10% data cases are poisoned using blended patterns with perturbation norm 1.0:
python train.py --sigma 2.0 --dldp_gnorm 5.0 --dldp_sigma 4.0 --dataset mnist --atk_method blending --poison_r 0.1 --delta 1.0
Evaluate:
python eval.py --sigma 2.0 --dldp_gnorm 5.0 --dldp_sigma 4.0 --dataset mnist --atk_method blending --poison_r 0.1 --delta 1.0
Evalute the certified robustness of KNN models with smoothing noise scale 1.0 on a Trojaned spam classification dataset, in which 2% data cases are poisoned using one-pixel patterns with perturbation norm 0.1:
python eval_knn.py --sigma 1.0 --dataset spam --atk_method onepixel --poison_r 0.02 --delta 0.1