A pytorch implementation of "Explaining and harnessing adversarial examples"
This code is a pytorch implementation of FGSM(Fast Gradient Sign Method).
In this code, I used FGSM to fool Inception v3.
The picture 'Giant Panda' is exactly the same as in the paper.
You can add other pictures with a folder with the label name in the 'data'.
- python==3.6
- numpy==1.14.2
- pytorch==1.0.0
- Mathmatical Results
- There are some important difference between adversarial training and L1 weight decay. (p.4)
- On logistic regression,
- Adversarial training : the L1 penalty is subtracted off inside of the activation during training.
- L1 weight decay : the L1 penalty is added to the training cost(=outside of the activation) during training.
- There are some important difference between adversarial training and L1 weight decay. (p.4)
- Experimental Results
- We can use FGSM for a regularizer but it does not defend against all adversarial attack images. (p.5)
- RBF networks are resistant to adversarial examples, but not for Linear. (p.7)
- The author claims current methodologies all resemble the linear classifier, which is why do adversarial examples generalize
- Alternative hypotheses(generative models with input distribution, ensembles) are not resistant to adversarial examples. (p.8)
- This Repository won't be updated.
- Please check the package of adversarial attacks in pytorch