adv-ml 0.0.4

A modular and easy-to-use framework of adversarial machine learning algorithms: https://en.m.wikipedia.org/wiki/Adversarial_machine_learning

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)