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advt-1.0.3
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مشاهده بیشترتوضیحات
A library for Attack & Defence on Video Task
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | advt-1.0.3 |
| نام | advt |
| نسخه کتابخانه | 1.0.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | WindF98 |
| ایمیل نویسنده | wwj98713@163.com |
| آدرس صفحه اصلی | https://github.com/WindFantasy98/ADVT |
| آدرس اینترنتی | https://pypi.org/project/advt/ |
| مجوز | - |
# Attack & Defence on Video Tasks
## Overview
**ADVT** (Attack Defence on Video Tasks) is an adversarial attack/defence toolbox open source library based on Pytorch.
This repository mainly implements some adversarial attack & defence algorithms and provides some video processing apis.
## Features
The ADVT library has five functional features, which cover the whole process:
- Preprocess
- Attack
- Defence
- Record
- Visualization
## Attack
This module implements attack methods. All attack methods are from top computer conferences in recent 5 years:
- [x] FGSM [Explaining and harnessing adversarial examples]
- [x] BIM [Adversarial examples in the physical world]
- [x] MIM [Boosting adversarial attacks with momentum(CVPR-18)]
- [x] DeepFool [DeepFool: A simple and accurate method to fool deep neural networks(CVPR-16)]
- [x] DIM [Improving Transferability of Adversarial Examples with Input Diversity(CVPR-18)]
- [x] C&W [Towards evaluating the robustness of neural networks(IEEE SP-17)]
- [x] Universal [Universal adversarial perturbations(CVPR-17)]
- [ ] ZOO [ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models(ACMAIS)]
- [x] Sparse ADV [Sparse Adversarial Perturbations for Videos(AAAI-19)]
## Defence
This module implements defence methods.
- [x] Bit-depth Reduction
- [x] Total Variance Minimization
- [ ] Image Quilting
- [x] ComDefend [ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples(CVPR-18)]
- [x] Randomization [Mitigating Adversarial Effects Through Randomization(ICLR-18)]
## Evaluation & Visualization
ADVT provides some useful evaluation & visualization tools.
**Evaluation Metric:**
- [ ] PSNR
- [ ] SSMI
- [ ] mAP
**Visualization:**
- [x] Video-to-Frames
- [x] Frames-to-Video
- [x] Changed-Pixel
## Installation
Before installation, make sure you install fellow requirements.
```shell script
numpy==1.18.5
opencv-python==4.4.0.42
torch==1.7.0
urllib==1.26.4
```
You can install advt through pypi or build from source code.
**1. from pypi**
```shell script
pip install advt
```
**2. from source code**
```shell script
git clone https://github.com/WindFantasy98/ADVT.git
pip install -e .
```
## Usage
1. attack example
```python
# python3.7 torch1.7
import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from advt.model.cnn import CNN
from advt.attack import FGSM
PATH_PARAMETERS = 'tests/cnn_model.pth'
def main():
transform = transforms.Compose([transforms.ToTensor()])
t = transforms.Compose([transforms.ToPILImage()])
test_dataset = datasets.CIFAR10(root='/data', train=False, transform=transform, download=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = CNN()
net.load_state_dict(torch.load(PATH_PARAMETERS))
net = net.to(device)
fgsm = FGSM(net, device)
attack_succ = 0
total_num = 0
for i, (img, lbl) in enumerate(test_loader):
img, lbl = img.to(device), lbl.to(device)
adv_img = fgsm.attack(img, lbl)
output = net(adv_img)
_, pred_indice = output.max(1)
total_num += len(lbl)
attack_succ += (pred_indice == lbl).sum().item()
if (i + 1) % 20 == 0:
print('batch {}:'.format((i + 1) // 20),
'total tested number: {}, correct number: {}'.format(total_num, attack_succ))
if __name__ == '__main__':
main()
```
2. defence example
```python
# python3.7 torch1.7
import torch
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from advt.model.cnn import CNN
from advt.attack import DeepFool
from advt.defence import Randomization
PATH_PARAMETERS = 'tests/cnn_model.pth'
def main():
# initialize dataset
transform = transforms.Compose([transforms.ToTensor()])
t = transforms.Compose([transforms.ToPILImage()])
test_dataset = datasets.CIFAR10(root='/data', train=False, transform=transform, download=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# load victim model
net = CNN()
net.load_state_dict(torch.load(PATH_PARAMETERS))
net = net.to(device)
# initialize attack method
df = DeepFool(net, device)
# initialize defend method
rand_defend = Randomization(net, device)
# initialize indicator
attack_succ = 0
total_num = 0
# start attack
for i, (img, lbl) in enumerate(test_loader):
img, lbl = img.to(device), lbl.to(device)
adv_img = df.attack(img, lbl) # get adv sample
output = rand_defend.defend(adv_img) # get processed sample
_, pred_indice = output.max(1)
total_num += len(lbl)
attack_succ += (pred_indice == lbl).sum().item()
if (i + 1) % 20 == 0:
print('batch {}:'.format((i + 1) // 20),
'total tested number: {}, correct number: {}'.format(total_num, attack_succ))
if __name__ == '__main__':
main()
```
This repo is still under maintenance. For more information, please contact with me.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl advt-1.0.3:
pip install advt-1.0.3.whl
نصب پکیج tar.gz advt-1.0.3:
pip install advt-1.0.3.tar.gz