pytorch-adversarial-examples
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PyTorch Implemetations of Adversarial Examples
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Adversarial Examples
PyTorch implementation following algorithms:
- Fast Gradient Sign Method (FGSM) [1]
- Basic Iterative Method (BIM) [2]
- DeepFool [3]
Prerequisites
- Python 3.5.2
- PyTorch 0.4.0
- torchvision 0.2.1
- NumPy 1.14.3
Usage
$ python main.py
Dataset
Results
Clean
| Label: 0 | Label: 1 | Label: 2 | Label: 3 | Label: 4 | Label: 5 | Label: 6 | Label: 7 | Label: 8 | Label: 9 |
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FGSM (eps=0.15)
| Label: 2 | Label: 8 | Label: 1 | Label: 2 | Label: 9 | Label: 3 | Label: 5 | Label: 2 | Label: 1 | Label: 7 |
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BIM (eps=0.15, eps_iter=0.01, n_iter=50)
| Label: 7 | Label: 8 | Label: 3 | Label: 2 | Label: 9 | Label: 3 | Label: 5 | Label: 2 | Label: 1 | Label: 7 |
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DeepFool (max_iter=50)
| Label: 9 | Label: 8 | Label: 3 | Label: 8 | Label: 9 | Label: 3 | Label: 5 | Label: 8 | Label: 3 | Label: 7 |
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Reference
[1] Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy. Explaining and Harnessing Adversarial Examples. ICLR, 2015
[2] Alexey Kurakin, Ian J. Goodfellow, Samy Bengio. Adversarial Examples in the Physical World. arXiv, 2016
[3] Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks. CVPR, 2016
Author
Kuan-Hao Huang / @ej0cl6
ej0cl6