adv-ml
Docs
See https://irad-zehavi.github.io/adv-ml/
Install
pip install adv_mlHow to use
How to Use
As an nbdev library, adv-ml supports import * (without importing
unwanted symbols):
from adv_ml.all import *Adversarial Examples
mnist = MNIST()
classifier = MLP(10)
learn = Learner(mnist.dls(), classifier, metrics=accuracy)
learn.fit(1)| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 0 | 0.154410 | 0.177410 | 0.953900 | 00:32 |
sub_dsets = mnist.valid.random_sub_dsets(64)
learn.show_results(shuffle=False, dl=sub_dsets.dl())
attack = InputOptimizer(classifier, LinfPGD(epsilon=.15), n_epochs=10, epoch_size=20)
perturbed_dsets = attack.perturb(sub_dsets)| epoch | train_loss | time |
|---|---|---|
| 0 | -4.302573 | 00:00 |
| 1 | -7.585707 | 00:00 |
| 2 | -9.014968 | 00:00 |
| 3 | -9.700548 | 00:00 |
| 4 | -10.075110 | 00:00 |
| 5 | -10.296636 | 00:00 |
| 6 | -10.433834 | 00:00 |
| 7 | -10.521141 | 00:00 |
| 8 | -10.577673 | 00:00 |
| 9 | -10.614740 | 00:00 |
learn.show_results(shuffle=False, dl=TfmdDL(perturbed_dsets))
Data Poisoning
patch = torch.tensor([[1, 0, 1],
[0, 1, 0],
[1, 0, 1]]).int()*255
trigger = F.pad(patch, (25, 0, 25, 0)).numpy()
learn = Learner(mnist.dls(), MLP(10), metrics=accuracy, cbs=BadNetsAttack(trigger, '0'))
learn.fit_one_cycle(1)| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 0 | 0.103652 | 0.097075 | 0.971400 | 00:23 |
Benign performance:
learn.show_results()
Attack success:
learn.show_results(2)
