EMI-FGSM
This repository contains code to reproduce results from the paper:
Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021)
Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong Wang, Kun He
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
Preparing data and models
You should download the data and pretrained models and place the data and pretrained models in dev_data/ and models/, respectively.
EMI-FGSM
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 Admix attack for example, you can run this attack as following:
CUDA_VISIBLE_DEVICES=gpuid python epi_fgsm.py
Evaluating the attack
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
Acknowledgments
Code refers to SI-NI-FGSM.
Contact
Questions and suggestions can be sent to [email protected].
