https://github.com/gralliry/adversarial-attack-generation-techniques
Adversarial attack generation techniques for CIFAR10 based on Pytorch: L-BFGS, FGSM, I-FGSM, MI-FGSM, DeepFool, C&W, JSMA, ONE-PIXEL, UPSET
https://github.com/gralliry/adversarial-attack-generation-techniques
cw deep-learning deepfool fgsm gan jsma neural-network one-pixel pytorch upset
Last synced: about 2 months ago
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Adversarial attack generation techniques for CIFAR10 based on Pytorch: L-BFGS, FGSM, I-FGSM, MI-FGSM, DeepFool, C&W, JSMA, ONE-PIXEL, UPSET
- Host: GitHub
- URL: https://github.com/gralliry/adversarial-attack-generation-techniques
- Owner: gralliry
- License: mit
- Created: 2024-01-12T05:16:27.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-02-22T18:52:01.000Z (4 months ago)
- Last Synced: 2025-03-29T09:51:17.097Z (2 months ago)
- Topics: cw, deep-learning, deepfool, fgsm, gan, jsma, neural-network, one-pixel, pytorch, upset
- Language: Python
- Homepage:
- Size: 1.3 MB
- Stars: 8
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Adversarial Attack Generation Techniques
[中文版本](./assets/README.cn.md)
## File overview
```
│
├── attack # The folder that contains the attack model class
│ └── fgsm.py
│ └── ...
│
├── datasets # Dataset folders
│ └── download.py # A script for downloading the dataset
│
├── parameter # Model Parameters folder
│ └── ResNet
│ │ └── ...pth
│ └── ...
│ │
│ └── UPSET # The perturbation used by UPSET generates a folder of model parameters
│ └── target_0 # Corresponds to the destination folder for perturbation generation model parameters
│ │ └── ...pth
│ └── target_1
│ └── ...
│
├── models
│ └── resnet.py # Identify model classes
│ └── ...
│
├── report # The folder used for the results of the attack effect
│
├── tensorboard # Tensorboard Training process log folder
│
├── contrast.py # The visualization of the attack effect requires a graphical interface
│
├── test.py # Test post-attack accuracy
│
├── test_num_workers.py # Test the optimal number of worker threads
│
├── train.py # Train a recognition model
│
└── train_residual_model.py # Train a perturbation generation model for the UPSET method
```
## Install
* Python-3.10 + Cuda-11.7 + Cudnn-8800
```shell
git clone https://github.com/gralliry/Adversarial-Attack-Generation-Techniques.git
cd Adversarial-Attack-Generation-Techniques
pip install -r requirements.txt
```
## Train the recognition model
```shell
# -e --epoch Specify the number of training epochs, optional, defaults to 100
python train.py -e 100
```
## Visualize the impact of the attack
```shell
# -m Specify the attack method
# L-BFGS, FGSM, I-FGSM, JSMA, ONE-PIXEL, C&W, DEEPFOOL, MI-FGSM, UPSET
python contrast.py -m FGSM
```
## Test post-attack accuracy
```shell
# -m Specify the attack method
# L-BFGS、FGSM、I-FGSM、JSMA、单像素、c&w、deepfool、mi-fgsm、翻转
# -c Specify the number of test runs, optional, defaults to 1000
python test.py -m FGSM -c 1000
```
## Exception: Perturbation generation model for training the UPSET method
The UPSET method requires a perturbation generation model to be trained separately
```shell
# -t --target Specifies the target label for the perturbation generation model
# The range is 0 ~ 9, corresponding to airplanes, cars, birds, cats, deer, dogs, frogs, horses, boats, trucks
# -e --epoch Specify the number of training epochs, optional, defaults to 100
# -lr --learning_rate Specify the learning rate, optional, defaults to 1e-3
# Training a perturbation model requires a trained recognition model, which can be modified or loaded in the script
python train_residual_model.py -t 0 -e 100
```
## Test the optimal number of worker threads
```shell
# Run and select the optimal number of worker threads based on execution time
python test_num_workers.py
```
## Contributors
- Chen Yiyi
- Americ Garcia