This is the official implementation for NeurIPS2022 paper:
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
Yiting Chen, Qibing Ren and Junchi Yan
The codebase is built and tested with Python 3.9.5. To install required packages:
pip install -r requrements.txt
To sample Shapley value, the first step is to train a ST model via
python train_model.py --config config/train_model.yaml
The checkpoint of the trained model will be saved in ./output/$date_of_the_time/train_model.yaml/params/
Change the shapley.model_path in config/Shapley.yaml to the path of checkpoint at the last epoch and sample the Shapley value via.
python Shapley_softmax.py --config config/Shapley.yaml
The results would be saved in ./output/$date_of_the_time/Shapley.yaml/shap_result
To train At models with CSA, the first step is get the NFCs and PFCs of each data sample:
python Reconstruct.py --shap_path ./output/$date_of_the_time/Shapley.yaml
where the reconstructed images of the NFCs and the PFCs of each data sample will be stored in ./output/$date_of_the_time/Shapley.yaml/ifft
You could also download the generated files here
Change the train.conf_path in config/madrys_CIFAR10_ResNet18_csa.yaml and config/trades_CIFAR10_ResNet18_csa.yaml to the path ./output/$date_of_the_time/Shapley.yaml/ifft or where ever you place the reconstructed images.
Train a ResNet18 under PGD-AT with CSA:
python train_model_adv_csa.py --config config/madrys_CIFAR10_ResNet18_csa.yaml
Train a ResNet18 under TRADES with CSA:
python train_model_adv_csa.py --config config/trades_CIFAR10_ResNet18_csa.yaml
We also provide checkpoints of model at the last epoch:
| Methods | Clean Acc | Acc under PGD-20 | Acc under Auto attack | Checkpoint |
|---|---|---|---|---|
| PGD-AT | 84.49 | 46.38 | 44.06 | ResNet18 |
| PGD-AT+CSA | 82.91 | 49.42 | 46.56 | ResNet18 |
| TRADES | 81.71 | 51.08 | 47.74 | ResNet18 |
| TRADES+CSA | 81.62 | 52.06 | 49.16 | ResNet18 |