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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 AttackModel, FGSM, MIFGSM

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.runner for a quick example.

Attacks

Gradient-based attacks:

Name	$\ell_p$	Publication	Paper (Open Access)	Class Name
FGSM	$\ell_\infty$	ICLR 2015	Explaining and Harnessing Adversarial Examples	FGSM
PGD	$\ell_\infty$	ICLR 2018	Towards Deep Learning Models Resistant to Adversarial Attacks	PGD
PGD (L2)	$\ell_2$	ICLR 2018	Towards Deep Learning Models Resistant to Adversarial Attacks	PGDL2
MI-FGSM	$\ell_\infty$	CVPR 2018	Boosting Adversarial Attacks with Momentum	MIFGSM
DI-FGSM	$\ell_\infty$	CVPR 2019	Improving Transferability of Adversarial Examples with Input Diversity	DIFGSM
TI-FGSM	$\ell_\infty$	CVPR 2019	Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks	TIFGSM
NI-FGSM	$\ell_\infty$	ICLR 2020	Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks	NIFGSM
SI-NI-FGSM	$\ell_\infty$	ICLR 2020	Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks	SINIFGSM
VMI-FGSM	$\ell_\infty$	CVPR 2021	Enhancing the Transferability of Adversarial Attacks through Variance Tuning	VMIFGSM
VNI-FGSM	$\ell_\infty$	CVPR 2021	Enhancing the Transferability of Adversarial Attacks through Variance Tuning	VNIFGSM
Admix	$\ell_\infty$	ICCV 2021	Admix: Enhancing the Transferability of Adversarial Attacks	Admix
FIA	$\ell_\infty$	ICCV 2021	Feature Importance-aware Transferable Adversarial Attacks	FIA
PNA-PatchOut	$\ell_\infty$	AAAI 2022	Towards Transferable Adversarial Attacks on Vision Transformers	PNAPatchOut
SSA	$\ell_\infty$	ECCV 2022	Frequency Domain Model Augmentation for Adversarial Attack	SSA
TGR	$\ell_\infty$	CVPR 2023	Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization	TGR
DeCoWA	$\ell_\infty$	AAAI 2024	Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping	DeCoWA
VDC	$\ell_\infty$	AAAI 2024	Improving the Adversarial Transferability of Vision Transformers with Virtual Dense Connection	VDC

Others:

Name	$\ell_p$	Publication	Paper (Open Access)	Class Name
DeepFool	$\ell_2$	CVPR 2016	DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks	DeepFool
GeoDA	$\ell_\infty$, $\ell_2$	CVPR 2020	GeoDA: A Geometric Framework for Black-box Adversarial Attacks	GeoDA
SSP	$\ell_\infty$	CVPR 2020	A Self-supervised Approach for Adversarial Robustness	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