# SVRE

**Repository Path**: HeJiaxing97/SVRE

## Basic Information

- **Project Name**: SVRE
- **Description**: 到随机优化的启发而设计随机方差减少梯度方法。SVRE可以在外部循环获得更准确的梯度更新，以避免糟糕的局部最优，从而使精心制作的对抗样本不会“过拟合 ”集成模型。因此，精心设计的对抗样本预计对其他未知模型具有更高的可迁移性。
- **Primary Language**: Unknown
- **License**: GPL-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-17
- **Last Updated**: 2025-06-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Stochastic Variance Reduced Ensemble (SVRE)
This repository contains code to reproduce results from the paper:
**Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability**(CVPR2022).
We provide an example of the SVRE method, and the complete experimental code and data will be released soon. 



## Datasets And models
To run the code, you should download pre-trained models and the data. 

Please place [pre-trained models](https://drive.google.com/drive/folders/10cFNVEhLpCatwECA6SPB-2g0q5zZyfaw) under the models/ directory.  

Please unzip the data and place the [data](https://www.kaggle.com/google-brain/nips-2017-adversarial-learning-development-set) under the dataset/ directory.


## Requirements
- Python >= 3.6.5

- Tensorflow-gpu >= 1.14.0 

- Numpy >= 1.15.4 

- opencv >= 3.4.2

- scipy >= 1.1.0

- pandas >= 1.0.1

- imageio >= 2.6.1

  

## File Description
- `SVRE-I-FGSM.py`,`Ens-I-FGSM.py`,`SVRE-MI-FGSM.py`,`Ens-MI-FGSM.py` :  Generate adversarial examples.

- `eval.py`: Eval the efficacy of attack methods.

- `./models`: Pre-trained models.

- `./nets`:  Code for model architecture.

- `./dataset`: The images used in the experiment and their label information. 

  


## Experiments
We provide an example of generating the adversarial examples on the ensemble of four normally trained models, ie. Inc-v3, Inc-v4, Res-15 and IncRes-v2, and test the transferability of the crafted adversaries on defense models.

To generate adversarial exmples of SVRE-I-FGSM and Ens-I-FGSM:
```
CUDA_VISIBLE_DEVICES=[gpu id] python SVRE-I-FGSM.py
CUDA_VISIBLE_DEVICES=[gpu id] python Ens-I-FGSM.py
```

To eval the efficacy of SVRE-I-FGSM and Ens-I-FGSM:
```
CUDA_VISIBLE_DEVICES=[gpu id] python eval.py  --eval_file ./results/SVRE-I-FGSM/
CUDA_VISIBLE_DEVICES=[gpu id] python eval.py  --eval_file ./results/Ens-I-FGSM/
```



## Acknowledgements

In order to ensure that our personal information is not leaked, we obtain the download link of the model from open source repositories, eg. [SI-NI-FGSM](https://github.com/JHL-HUST/SI-NI-FGSM). We thank the authors for sharing.