Awesome
Variance Tuning
This repository contains code to reproduce results from the paper:
Enhancing the Transferability of Adversarial Attacks through Variance Tuning (CVPR 2021)
Xiaosen Wang, Kun He
We also include the torch version code in the framework TransferAttack.
Requirements
- Python >= 3.6.5
- Tensorflow >= 1.12.0
- Numpy >= 1.15.4
- opencv >= 3.4.2
- scipy > 1.1.0
- pandas >= 1.0.1
- imageio >= 2.6.1
Qucik Start
Prepare the data and models
You should download the data and pretrained models and place the data and pretrained models in dev_data/ and models/, respectively.
Variance Tuning Attack
All the provided codes generate adversarial examples on inception_v3 model. If you want to attack other models, replace the model in graph
and batch_grad
function and load such models in main
function.
Runing attack
Taking vmi_di_ti_si_fgsm attack for example, you can run this attack as following:
CUDA_VISIBLE_DEVICES=gpuid python vmi_di_ti_si_fgsm.py
The generated adversarial examples would be stored in directory ./outputs
. Then run the file simple_eval.py
to evaluate the success rate of each model used in the paper:
CUDA_VISIBLE_DEVICES=gpuid python simple_eval.py
EVaulations setting for Table 4
- HGD, R&P, NIPS-r3: We directly run the code from the corresponding repo.
- Bit-Red: step_num=4, alpha=200, base_model=Inc_v3_ens.
- JPEG: No extra parameters.
- FD: resize to 304*304 for FD and then resize back to 299*299, base_model=Inc_v3_ens
- ComDefend: resize to 224*224 for ComDefend and then resize back to 299*299, base_model=Resnet_101
- RS: noise=0.25, N=100, skip=100
- NRP: purifier=NRP, dynamic=True, base_model=Inc_v3_ens
More details in third_party
Acknowledgments
Code refers to SI-NI-FGSM.
Contact
Questions and suggestions can be sent to xswanghuster@gmail.com.