This library contains various resources related to adversarial attacks implemented in PyTorch. It is aimed towards researchers looking for implementations of state-of-the-art attacks.
The code was written to maximize efficiency (e.g. by preferring low level functions from PyTorch) while retaining simplicity (e.g. by avoiding abstractions). As a consequence, most of the library, and especially the attacks, is implemented using pure functions (whenever possible).
While focused on attacks, this library also provides several utilities related to adversarial attacks: distances (SSIM, CIEDE2000, LPIPS), visdom callback, projections, losses and helper functions. Most notably the function run_attack from utils/attack_utils.py performs an attack on a model given the inputs and labels, with fixed batch size, and reports complexity related metrics (run-time and forward/backward propagations).
The goal of this library is to be up-to-date with newer versions of PyTorch so the dependencies are expected to be updated regularly (possibly resulting in breaking changes).
- pytorch>=1.8.0
- torchvision>=0.9.0
- tqdm>=4.48.0
- visdom>=0.1.8
You can either install using:
pip install git+https://github.com/jeromerony/adversarial-library
Or you can clone the repo and run:
python setup.py install
Alternatively, you can install (after cloning) the library in editable mode:
pip install -e .
For an example on how to use this library, you can look at this repo: https://github.com/jeromerony/augmented_lagrangian_adversarial_attacks
Currently the following attacks are implemented in the adv_lib.attacks module:
| Name | Knowledge | Type | Distance(s) | ArXiv Link |
|---|---|---|---|---|
| Carlini and Wagner (C&W) | White-box | Minimal | L2, L∞ | 1608.04644 |
| Projected Gradient Descent (PGD) | White-box | Budget | L∞ | 1706.06083 |
| Structured Adversarial Attack (StrAttack) | White-box | Minimal | L2 + group-sparsity | 1808.01664 |
| Decoupled Direction and Norm (DDN) | White-box | Minimal | L2 | 1811.09600 |
| Trust Region (TR) | White-box | Minimal | L2, L∞ | 1812.06371 |
| Fast Adaptive Boundary (FAB) | White-box | Minimal | L1, L2, L∞ | 1907.02044 |
| Perceptual Color distance Alternating Loss (PerC-AL) | White-box | Minimal | CIEDE2000 | 1911.02466 |
| Auto-PGD (APGD) | White-box | Budget | L1, L2, L∞ | 2003.01690 2103.01208 |
| Augmented Lagrangian Method for Adversarial (ALMA) | White-box | Minimal | L1, L2, SSIM, CIEDE2000, LPIPS, ... | 2011.11857 |
| Folded Gaussian Attack (FGA) Voting Folded Gaussian Attack (VFGA) |
White-box | Minimal | L0 | 2011.12423 |
| Fast Minimum-Norm (FMN) | White-box | Minimal | L0, L1, L2, L∞ | 2102.12827 |
| Primal-Dual Gradient Descent (PDGD) Primal-Dual Proximal Gradient Descent (PDPGD) |
White-box | Minimal | L2 L0, L1, L2, L∞ |
2106.01538 |
Bold means that this repository contains the official implementation.
Type refers to the goal of the attack:
- Minimal attacks aim to find the smallest adversarial perturbation w.r.t. a given distance;
- Budget attacks aim to find an adversarial perturbation within a distance budget (and often to maximize a loss as well).
The following distances are available in the utils adv_lib.distances module:
- Lp-norms
- SSIM https://ece.uwaterloo.ca/~z70wang/research/ssim/
- MS-SSIM https://ece.uwaterloo.ca/~z70wang/publications/msssim.html
- CIEDE2000 color difference http://www2.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf
- LPIPS https://arxiv.org/abs/1801.03924
Suggestions and contributions are welcome :)
If this library has been useful for your research, you can cite it using the "Cite this repository" button in the "About" section.