Foolattack 0.0.1
A small example package
Requires Python: >=3.6

# Buddhu

# Adversarial-Attacks-PyTorch

<p>
  <a href="https://github.com/akshay-gupta123/Buddhu/blob/main/LICENSE"><img alt="MIT License" src="https://img.shields.io/github/license/akshay-gupta123/Buddhu" /></a>
  <a href="https://img.shields.io/pypi/v/torchattacks"><img alt="Pypi" src="https://img.shields.io/pypi/v/torchattacks.svg" /></a>
</p>

**Buddhu is a PyTorch library that contains adversarial attacks to generate adversarial examples.


<p align="center">

|                         Clean Image                          |                      Adversarial Image                       |
| :----------------------------------------------------------: | :----------------------------------------------------------: |
| <img src="https://github.com/Harry24k/adversairal-attacks-pytorch/blob/master/pic/clean.png" width="300" height="300"> | <img src="https://github.com/Harry24k/adversairal-attacks-pytorch/blob/master/pic/pgd.png" width="300" height="300"> |

</p>

## Table of Contents
1. [Usage](#Usage)
2. [Attacks and Papers](#Attacks-and-Papers)
3. [Contribution](#Contribution)
4. [Recommended Sites and Packages](#Recommended-Sites-and-Packages)



## Usage

### :clipboard: Dependencies

- torch==1.4.0
- python==3.6



### :hammer: Installation

- `pip install torchattacks` or
- `git clone https://github.com/Harry24k/adversairal-attacks-pytorch`

```python
import torchattacks
atk = torchattacks.PGD(model, eps=8/255, alpha=2/255, steps=4)
adversarial_images = atk(images, labels)
```



###  :warning: Precautions

* **All images should be scaled to [0, 1] with transform[to.Tensor()] before used in attacks.** To make it easy to use adversarial attacks, a reverse-normalization is not included in the attack process. To apply an input normalization, please add a normalization layer to the model. Please refer to [code](https://github.com/Harry24k/adversarial-attacks-pytorch/blob/master/demos/White%20Box%20Attack%20(ImageNet).ipynb) or [nbviewer](https://nbviewer.jupyter.org/github/Harry24k/adversarial-attacks-pytorch/blob/master/demos/White%20Box%20Attack%20%28ImageNet%29.ipynb).

* **All models should return ONLY ONE vector of `(N, C)` where `C = number of classes`.** Considering most models in _torchvision.models_ return one vector of `(N,C)`, where `N` is the number of inputs and `C` is thenumber of classes, _torchattacks_ also only supports limited forms of output.  Please check the shape of the model’s output carefully. In the case of the model returns multiple outputs, please refer to [the demo](https://github.com/Harry24k/adversarial-attacks-pytorch/blob/master/demos/Model%20with%20Multiple%20Outputs.ipynb).

* **`torch.backends.cudnn.deterministic = True` to get same adversarial examples with fixed random seed**. Some operations are non-deterministic with float tensors on GPU [[discuss]](https://discuss.pytorch.org/t/inconsistent-gradient-values-for-the-same-input/26179). If you want to get same results with same inputs, please run `torch.backends.cudnn.deterministic = True`[[ref]](https://stackoverflow.com/questions/56354461/reproducibility-and-performance-in-pytorch).




## Attacks and Papers

Implemented adversarial attacks in the papers.

The distance measure in parentheses.

|          Name          | Paper                                                        | Remark                                                       |
| :--------------------: | ------------------------------------------------------------ | ------------------------------------------------------------ |
|  **FGSM**<br />(Linf)  | Explaining and harnessing adversarial examples ([Goodfellow et al., 2014](https://arxiv.org/abs/1412.6572)) |                                                              |
|  **BIM**<br />(Linf)   | Adversarial Examples in the Physical World ([Kurakin et al., 2016](https://arxiv.org/abs/1607.02533)) | Basic iterative method or Iterative-FSGM                     |
|    **CW**<br />(L2)    | Towards Evaluating the Robustness of Neural Networks ([Carlini et al., 2016](https://arxiv.org/abs/1608.04644)) |                                                              |
| **RFGSM**<br />(Linf)  | Ensemble Adversarial Traning: Attacks and Defences ([Tramèr et al., 2017](https://arxiv.org/abs/1705.07204)) | Random initialization + FGSM                                 |
|  **PGD**<br />(Linf)   | Towards Deep Learning Models Resistant to Adversarial Attacks ([Mardry et al., 2017](https://arxiv.org/abs/1706.06083)) | Projected Gradient Method                                    |
|  **PGDL2**<br />(L2)   | Towards Deep Learning Models Resistant to Adversarial Attacks ([Mardry et al., 2017](https://arxiv.org/abs/1706.06083)) | Projected Gradient Method                                    |
| **DeepFool**<br />(L2) | DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks ([Moosavi-Dezfooli et al., 2016](https://arxiv.org/abs/1511.04599)) |                               |
| **OnePixel**<br />(L0) | One pixel attack for fooling deep neural networks ([Su et al., 2019](https://arxiv.org/abs/1710.08864)) |                                |
| **DI2FGSM**<br />(Linf) | Improving Transferability of Adversarial Examples with Input Diversity ([Xie et al., 2019](https://arxiv.org/abs/1803.06978)) |                                  |

## Contribution

This library is developed as a part of my learning, if you find any **bug** feel free to create a PR. All kind of contributions are always welcome! 


##  References

* [https://github.com/IBM/adversarial-robustness-toolbox](https://github.com/IBM/adversarial-robustness-toolbox)
* [https://github.com/bethgelab/foolbox](https://github.com/bethgelab/foolbox)(http://bethgelab.org/)
* [https://github.com/tensorflow/cleverhans](https://github.com/tensorflow/cleverhans)