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Description:
advml 0.0.4
adv-ml
Docs
See https://irad-zehavi.github.io/adv-ml/
Install
pip install adv_ml
How 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)
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
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