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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: about 2 months 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 (over 5 years ago)
- Last Synced: 2024-11-04T13:37:49.092Z (about 2 months ago)
- Topics: adversarial-attacks, deep-learning, pytorch
- Language: Jupyter Notebook
- Size: 920 KB
- Stars: 53
- Watchers: 2
- Forks: 11
- Open Issues: 2
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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)