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.

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