nebula-beta / Torchadver
A PyTorch Toolbox for creating adversarial examples that fool neural networks.
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Introduction
torchadver is a Pytorch tool box for generating adversarial images. The basic adversarial attack are implemented. Such as FSGM, I-FGSM, MI-FGSM, M-DI-FGSM, C&W .etc.
Installation
How to Use
The brief attack process is shown below. More detailed process introduction you can refer to ./examples/toturial.py.
Generate adversarial images by satisfy L2 norm
Non-targeted attack
from torchadver.attacker.iterative_gradient_attack import FGM_L2, I_FGM_L2, MI_FGM_L2, M_DI_FGM_L2
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
# images normalized by mean and std
images, labels = ...
model = ...
# use mean and std to determine effective range of pixel of image in channels.
attacker = FGM_L2(model, loss_fn=nn.CrossEntropyLoss(),
mean=mean, std=std,
max_norm=4.0, # L2 norm bound
random_init=True)
# for non-targeted attack
adv_images = attacker.attack(images, labels) # or adv_images = attacker.attack(images)
Targeted attack
from torchadver.attacker.iterative_gradient_attack import FGM_L2, I_FGM_L2, MI_FGM_L2, M_DI_FGM_L2
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
# images normalized by mean and std
images, labels = ...
model = ...
targeted_labels = ...
# use mean and std to determine effective range of pixel of image in channels.
attacker = FGM_L2(model, loss_fn=nn.CrossEntropyLoss(),
mean=mean, std=std,
max_norm=4.0, # L2 norm bound
random_init=True)
# for non-targeted attack
adv_images = attacker.attack(images, targeted_labels)
Generate adversarial images by satisfy Linf norm
Non-targeted attack
from torchadver.attacker.iterative_gradient_attack import FGM_LInf, I_FGM_LInf, MI_FGM_LInf, M_DI_FGM_LInf
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
# images normalized by mean and std
images, labels = ...
model = ...
# use mean and std to determine effective range of pixel of image in channels.
attacker = FGM_L2(model, loss_fn=nn.CrossEntropyLoss(),
mean=mean, std=std,
max_norm=0.1, # Linf norm bound
random_init=True)
# for non-targeted attack
adv_images = attacker.attack(images, labels) # or adv_images = attacker.attack(images)
Targeted attack
from torchadver.attacker.iterative_gradient_attack import FGM_LInf, I_FGM_LInf, MI_FGM_LInf, M_DI_FGM_LInf
mean = [0.5, 0.5, 0.5]
std = [0.5, 0.5, 0.5]
# images normalized by mean and std
images, labels = ...
model = ...
targeted_labels = ...
# use mean and std to determine effective range of pixel of image in channels.
attacker = FGM_L2(model, loss_fn=nn.CrossEntropyLoss(),
mean=mean, std=std,
max_norm=0.1, # Linf norm bound
random_init=True, targeted=True)
# for non-targeted attack
adv_images = attacker.attack(images, targeted_labels)
Citations
More information about adversarial attack about deep learning, refer to awesome-adversarial-deep-learning.
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