Adversarial Library

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).

Dependencies

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

Installation

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 .

Usage

Attacks are implemented as functions, so they can be called directly by providing the model, samples and labels (possibly with optional arguments): python from adv_lib.attacks import ddn adv_samples = ddn(model=model, inputs=inputs, labels=labels, steps=300)

Classification attacks all expect the following arguments: - model: the model that produces logits (pre-softmax activations) with inputs in $[0, 1]$ - inputs: the samples to attack in $[0, 1]$ - labels: either the ground-truth labels for the samples or the targets - targeted: flag indicated if the attack should be targeted or not -- defaults to False

Additionally, many attacks have an optional callback argument which accepts an adv_lib.utils.visdom_logger.VisdomLogger to plot data to a visdom server for monitoring purposes.

For a more detailed example on how to use this library, you can look at this repo: https://github.com/jeromerony/augmentedlagrangianadversarial_attacks

Contents

Attacks

Classification

Currently the following classification attacks are implemented in the adv_lib.attacks module:

| Name | Knowledge | Type | Distance(s) | ArXiv Link | |-----------------------------------------------------------------------------------------|-----------|---------|-----------------------------------------------------------|------------------------------------------------------------------------------------------------------| | DeepFool (DF) | White-box | Minimal | $\ell2$, $\ell\infty$ | 1511.04599 | | Carlini and Wagner (C&W) | White-box | Minimal | $\ell2$, $\ell\infty$ | 1608.04644 | | Projected Gradient Descent (PGD) | White-box | Budget | $\ell\infty$ | 1706.06083 | | Structured Adversarial Attack (StrAttack) | White-box | Minimal | $\ell2$ + group-sparsity | 1808.01664 | | Decoupled Direction and Norm (DDN) | White-box | Minimal | $\ell2$ | 1811.09600 | | Trust Region (TR) | White-box | Minimal | $\ell2$, $\ell\infty$ | 1812.06371 | | Fast Adaptive Boundary (FAB) | White-box | Minimal | $\ell1$, $\ell2$, $\ell\infty$ | 1907.02044 | | Perceptual Color distance Alternating Loss (PerC-AL) | White-box | Minimal | CIEDE2000 | 1911.02466 | | Auto-PGD (APGD) | White-box | Budget | $\ell1$, $\ell2$, $\ell\infty$ | 2003.01690
2103.01208 | | Augmented Lagrangian Method for Adversarial (ALMA) | White-box | Minimal | $\ell1$, $\ell2$, SSIM, CIEDE2000, LPIPS, ... | 2011.11857 | | Folded Gaussian Attack (FGA)
Voting Folded Gaussian Attack (VFGA) | White-box | Minimal | $\ell0$ | 2011.12423 | | Fast Minimum-Norm (FMN) | White-box | Minimal | $\ell0$, $\ell1$, $\ell2$, $\ell\infty$ | 2102.12827 | | Primal-Dual Gradient Descent (PDGD)
Primal-Dual Proximal Gradient Descent (PDPGD) | White-box | Minimal | $\ell2$
$\ell0$, $\ell1$, $\ell2$, $\ell\infty$ | 2106.01538 | | SuperDeepFool (SDF) | White-box | Minimal | $\ell2$ | 2303.12481 | | σ-zero | White-box | Minimal | $\ell_0$ | 2402.01879 |

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).

Segmentation

The library now includes segmentation attacks in the adv_lib.attacks.segmentation module. These require the following arguments: - model: the model that produces logits (pre-softmax activations) with inputs in $[0, 1]$ - inputs: the images to attack in $[0, 1]$. Shape: $b\times c\times h\times w$ with $b$ the batch size, $c$ the number of color channels and $h$ and $w$ the height and width of the images. - labels: either the ground-truth labels for the samples or the targets. Shape: $b\times h\times w$. - masks: binary mask indicating which pixels to attack, to account for unlabeled pixels (e.g. void in Pascal VOC). Shape: $b\times h\times w$ - targeted: flag indicated if the attack should be targeted or not -- defaults to False - adv_threshold: fraction of the pixels to consider an attack successful -- defaults to 0.99

The following segmentation attacks are implemented:

| Name | Knowledge | Type | Distance(s) | ArXiv Link | |-------------------------------------------------------------------------------------------|-----------|---------|-----------------------------------------------------------|------------------------------------------------| | Dense Adversary Generation (DAG) | White-box | Minimal | $\ell2$, $\ell\infty$ | 1703.08603 | | Adaptive Segmentation Mask Attack (ASMA) | White-box | Minimal | $\ell2$ | 1907.13124 | | _Primal-Dual Gradient Descent (PDGD)
Primal-Dual Proximal Gradient Descent (PDPGD) | White-box | Minimal | $\ell2$
$\ell0$, $\ell1$, $\ell2$, $\ell\infty$ | 2106.01538 | | ALMA prox | White-box | Minimal | $\ell\infty$ | 2206.07179 |

Italic indicates that the attack is unofficially adapted from the classification variant.

Distances

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

Contributions

Suggestions and contributions are welcome :)

Citation

If this library has been useful for your research, you can cite it using the "Cite this repository" button in the "About" section.