https://github.com/Harry24k/CW-pytorch

A pytorch implementation of "Towards Evaluating the Robustness of Neural Networks"
https://github.com/Harry24k/CW-pytorch

adversarial-attacks deep-learning pytorch

Last synced: 9 days ago
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A pytorch implementation of "Towards Evaluating the Robustness of Neural Networks"

• Host: GitHub
• URL: https://github.com/Harry24k/CW-pytorch
• Owner: Harry24k
• License: mit
• Created: 2019-04-11T06:43:54.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-09-04T14:46:36.000Z (about 5 years ago)
• Last Synced: 2024-08-01T13:23:18.274Z (3 months ago)
• Topics: adversarial-attacks, deep-learning, pytorch
• Language: Jupyter Notebook
• Size: 920 KB
• Stars: 51
• Watchers: 2
• Forks: 11
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# CW-pytorch

**A pytorch implementation of "[Towards Evaluating the Robustness of Neural Networks](https://arxiv.org/abs/1608.04644)"**

## Summary

This code is a pytorch implementation of **CW attack**.   

In this code, I used above methods to fool [Inception v3](https://arxiv.org/abs/1512.00567).   

'[Giant Panda](http://www.image-net.org/)' used for an example.   

You can add other pictures with a folder with the label name in the 'data/imagenet'.    

## Requirements

* python==3.6   

* numpy==1.14.2   

* pytorch==1.0.1   

## Important results not in the code

- This paper suggested new approach to the adversarial attack.(p.6-7)

    - Compared 7 new objective functions(*f*) for generating adversarial images.

    - Used *tanh* for solving box constraints.

- Three new attack algorithms proposed.(p.9-10)

    - They are L_2, L_0, L_inf. (Among these, L_2 is implementd in this code)

- These attacks made the [defensive distillation](https://arxiv.org/abs/1511.04508) helpless.(p.12-14)

    - All of new attack methods succeed with 100% success.

## Notice

- This Repository won't be updated.

- Please check [the package of adversarial attacks in pytorch](https://github.com/Harry24k/adversairal-attacks-pytorch)