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🛡 torchattack - A set of adversarial attacks in PyTorch.

_{Install from GitHub source -}

python -m pip install git+https://github.com/spencerwooo/torchattack

_{Install from Gitee mirror -}

python -m pip install git+https://gitee.com/spencerwoo/torchattack

Usage

import torch
from torchattack import FGSM, MIFGSM
from torchattack.eval import AttackModel

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load a model
model = AttackModel.from_pretrained(model_name='resnet50', device=device)
transform, normalize = model.transform, model.normalize

# Initialize an attack
attack = FGSM(model, normalize, device)

# Initialize an attack with extra params
attack = MIFGSM(model, normalize, device, eps=0.03, steps=10, decay=1.0)

Check out torchattack.eval.run_attack for a simple example.

Attacks

Gradient-based attacks:

Name	$\ell_p$	Paper	torchattack class
FGSM	$\ell_\infty$	Explaining and Harnessing Adversarial Examples	torchattack.FGSM
PGD	$\ell_\infty$	Towards Deep Learning Models Resistant to Adversarial Attacks	torchattack.PGD
PGD (L2)	$\ell_2$	Towards Deep Learning Models Resistant to Adversarial Attacks	torchattack.PGDL2
MI-FGSM	$\ell_\infty$	Boosting Adversarial Attacks with Momentum	torchattack.MIFGSM
DI-FGSM	$\ell_\infty$	Improving Transferability of Adversarial Examples with Input Diversity	torchattack.DIFGSM
TI-FGSM	$\ell_\infty$	Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks	torchattack.TIFGSM
NI-FGSM	$\ell_\infty$	Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks	torchattack.NIFGSM
SI-NI-FGSM	$\ell_\infty$	Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks	torchattack.SINIFGSM
VMI-FGSM	$\ell_\infty$	Enhancing the Transferability of Adversarial Attacks through Variance Tuning	torchattack.VMIFGSM
VNI-FGSM	$\ell_\infty$	Enhancing the Transferability of Adversarial Attacks through Variance Tuning	torchattack.VNIFGSM
Admix	$\ell_\infty$	Admix: Enhancing the Transferability of Adversarial Attacks	torchattack.Admix
FIA	$\ell_\infty$	Feature Importance-aware Transferable Adversarial Attacks	torchattack.FIA
PNA-PatchOut	$\ell_\infty$	Towards Transferable Adversarial Attacks on Vision Transformers	torchattack.PNAPatchOut
TGR	$\ell_\infty$	Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization	torchattack.TGR
DeCoWA	$\ell_\infty$	Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping	torchattack.DeCoWA

Others:

Name	$\ell_p$	Paper	torchattack class
DeepFool	$\ell_2$	DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks	torchattack.DeepFool
GeoDA	$\ell_\infty$, $\ell_2$	GeoDA: A Geometric Framework for Black-box Adversarial Attacks	torchattack.GeoDA
SSP	$\ell_\infty$	A Self-supervised Approach for Adversarial Robustness	torchattack.SSP

Development

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate

# Install deps with dev extras
python -m pip install -r requirements.txt
python -m pip install -e '.[dev]'

License

MIT

Related

• Harry24k/adversarial-attacks-pytorch
• Trusted-AI/adversarial-robustness-toolbox