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Fix mypy, update README
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@spencerwooo
spencerwooo committed Nov 20, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/lint.yml
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Expand Up @@ -39,4 +39,4 @@ jobs:
- name: Mypy
run: |
mypy src/
mypy torchattack
50 changes: 25 additions & 25 deletions README.md
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Expand Up @@ -57,33 +57,33 @@ Check out [`torchattack.eval.run_attack`](src/torchattack/eval.py) for a simple

Gradient-based attacks:

| Name | $\ell_p$ | Paper | `torchattack` class |
| :----------: | :-----------: | :------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------ |
| FGSM | $\ell_\infty$ | [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572) | `torchattack.FGSM` |
| PGD | $\ell_\infty$ | [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083) | `torchattack.PGD` |
| PGD (L2) | $\ell_2$ | [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083) | `torchattack.PGDL2` |
| MI-FGSM | $\ell_\infty$ | [Boosting Adversarial Attacks with Momentum](https://arxiv.org/abs/1710.06081) | `torchattack.MIFGSM` |
| DI-FGSM | $\ell_\infty$ | [Improving Transferability of Adversarial Examples with Input Diversity](https://arxiv.org/abs/1803.06978) | `torchattack.DIFGSM` |
| TI-FGSM | $\ell_\infty$ | [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://arxiv.org/abs/1904.02884) | `torchattack.TIFGSM` |
| NI-FGSM | $\ell_\infty$ | [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://arxiv.org/abs/1908.06281) | `torchattack.NIFGSM` |
| SI-NI-FGSM | $\ell_\infty$ | [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://arxiv.org/abs/1908.06281) | `torchattack.SINIFGSM` |
| VMI-FGSM | $\ell_\infty$ | [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://arxiv.org/abs/2103.15571) | `torchattack.VMIFGSM` |
| VNI-FGSM | $\ell_\infty$ | [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://arxiv.org/abs/2103.15571) | `torchattack.VNIFGSM` |
| Admix | $\ell_\infty$ | [Admix: Enhancing the Transferability of Adversarial Attacks](https://arxiv.org/abs/2102.00436) | `torchattack.Admix` |
| FIA | $\ell_\infty$ | [Feature Importance-aware Transferable Adversarial Attacks](https://arxiv.org/abs/2107.14185) | `torchattack.FIA` |
| PNA-PatchOut | $\ell_\infty$ | [Towards Transferable Adversarial Attacks on Vision Transformers](https://arxiv.org/abs/2109.04176) | `torchattack.PNAPatchOut` |
| SSA | $\ell_\infty$ | [Frequency Domain Model Augmentation for Adversarial Attack](https://arxiv.org/abs/2207.05382) | `torchattack.SSA` |
| TGR | $\ell_\infty$ | [Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization](https://arxiv.org/abs/2303.15754) | `torchattack.TGR` |
| DeCoWA | $\ell_\infty$ | [Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping](https://arxiv.org/abs/2402.03951) | `torchattack.DeCoWA` |
| VDC | $\ell_\infty$ | [Improving the Adversarial Transferability of Vision Transformers with Virtual Dense Connection](https://ojs.aaai.org/index.php/AAAI/article/view/28541) | `torchattack.VDC` |
| Name | $\ell_p$ | Publication | Paper (Open Access) | Class Name |
| :----------: | :-----------: | :---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------- |
| FGSM | $\ell_\infty$ | ICLR 2015 | [Explaining and Harnessing Adversarial Examples](https://arxiv.org/abs/1412.6572) | `FGSM` |
| PGD | $\ell_\infty$ | ICLR 2018 | [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083) | `PGD` |
| PGD (L2) | $\ell_2$ | ICLR 2018 | [Towards Deep Learning Models Resistant to Adversarial Attacks](https://arxiv.org/abs/1706.06083) | `PGDL2` |
| MI-FGSM | $\ell_\infty$ | CVPR 2018 | [Boosting Adversarial Attacks with Momentum](https://arxiv.org/abs/1710.06081) | `MIFGSM` |
| DI-FGSM | $\ell_\infty$ | CVPR 2019 | [Improving Transferability of Adversarial Examples with Input Diversity](https://arxiv.org/abs/1803.06978) | `DIFGSM` |
| TI-FGSM | $\ell_\infty$ | CVPR 2019 | [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://arxiv.org/abs/1904.02884) | `TIFGSM` |
| NI-FGSM | $\ell_\infty$ | ICLR 2020 | [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://arxiv.org/abs/1908.06281) | `NIFGSM` |
| SI-NI-FGSM | $\ell_\infty$ | ICLR 2020 | [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://arxiv.org/abs/1908.06281) | `SINIFGSM` |
| VMI-FGSM | $\ell_\infty$ | CVPR 2021 | [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://arxiv.org/abs/2103.15571) | `VMIFGSM` |
| VNI-FGSM | $\ell_\infty$ | CVPR 2021 | [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://arxiv.org/abs/2103.15571) | `VNIFGSM` |
| Admix | $\ell_\infty$ | ICCV 2021 | [Admix: Enhancing the Transferability of Adversarial Attacks](https://arxiv.org/abs/2102.00436) | `Admix` |
| FIA | $\ell_\infty$ | ICCV 2021 | [Feature Importance-aware Transferable Adversarial Attacks](https://arxiv.org/abs/2107.14185) | `FIA` |
| PNA-PatchOut | $\ell_\infty$ | AAAI 2022 | [Towards Transferable Adversarial Attacks on Vision Transformers](https://arxiv.org/abs/2109.04176) | `PNAPatchOut` |
| SSA | $\ell_\infty$ | ECCV 2022 | [Frequency Domain Model Augmentation for Adversarial Attack](https://arxiv.org/abs/2207.05382) | `SSA` |
| TGR | $\ell_\infty$ | CVPR 2023 | [Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization](https://arxiv.org/abs/2303.15754) | `TGR` |
| DeCoWA | $\ell_\infty$ | AAAI 2024 | [Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping](https://arxiv.org/abs/2402.03951) | `DeCoWA` |
| VDC | $\ell_\infty$ | AAAI 2024 | [Improving the Adversarial Transferability of Vision Transformers with Virtual Dense Connection](https://ojs.aaai.org/index.php/AAAI/article/view/28541) | `VDC` |

Others:

| Name | $\ell_p$ | Paper | `torchattack` class |
| :------: | :---------------------: | :------------------------------------------------------------------------------------------------------ | :--------------------- |
| DeepFool | $\ell_2$ | [DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks](https://arxiv.org/abs/1511.04599) | `torchattack.DeepFool` |
| GeoDA | $\ell_\infty$, $\ell_2$ | [GeoDA: A Geometric Framework for Black-box Adversarial Attacks](https://arxiv.org/abs/2003.06468) | `torchattack.GeoDA` |
| SSP | $\ell_\infty$ | [A Self-supervised Approach for Adversarial Robustness](https://arxiv.org/abs/2006.04924) | `torchattack.SSP` |
| 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](https://arxiv.org/abs/1511.04599) | `DeepFool` |
| GeoDA | $\ell_\infty$, $\ell_2$ | CVPR 2020 | [GeoDA: A Geometric Framework for Black-box Adversarial Attacks](https://arxiv.org/abs/2003.06468) | `GeoDA` |
| SSP | $\ell_\infty$ | CVPR 2020 | [A Self-supervised Approach for Adversarial Robustness](https://arxiv.org/abs/2006.04924) | `SSP` |

## Development

Expand All @@ -94,7 +94,7 @@ source .venv/bin/activate

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

## License
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