EMI-FGSM

This repository contains code to reproduce results from the paper:

Boosting Adversarial Transferability with Enhanced Momentum (BMVC 2021)

Xiaosen Wang, Jiadong Lin, Han Hu, Jingdong 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

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 xswanghuster@gmail.com.