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TrojanZoo provides a universal pytorch platform to conduct security researches (especially backdoor attacks/defenses) of image classification in deep learning.
| Date | Stars |
|---|---|
| 2026-07-31 | 302 |
| 2026-08-06 | 303 |
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# TrojanZoo  [](mailto:[email protected]) [](https://opensource.org/licenses/GPL-3.0)  [](https://ain-soph.github.io/trojanzoo/) [](https://github.com/ain-soph/trojanzoo/releases) [](https://pypi.org/project/trojanzoo/) [](https://hub.docker.com/r/local0state/trojanzoo) <!-- [](https://anaconda.org/anaconda/trojanzoo) --> > **NOTE:** TrojanZoo requires `python>=3.11`, `pytorch>=2.0.0` and `torchvision>=0.15.0`, which must be installed manually. Recommend to use `conda` to install. This is the code implementation (pytorch) for our paper in EuroS&P 2022: [TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors](https://arxiv.org/abs/2012.09302) TrojanZoo provides a universal pytorch platform to conduct security researches (especially backdoor attacks/defenses) of image classification in deep learning. It is composed of two packages: `trojanzoo` and `trojanvision`. `trojanzoo` contains abstract classes and utilities, while `trojanvision` contains abstract and concrete ones for image classification task. > Note: This repository is also maintained to cover the implementation of > our kdd 2020 paper [AdvMind: Inferring Adversary Intent of Black-Box Attacks](https://arxiv.org/abs/2006.09539) > and ccs 2020 paper [A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models](https://arxiv.org/abs/1911.01559) ## Documentation We have documentation available at https://ain-soph.github.io/trojanzoo. ## Screenshot  ## Features 1. **Colorful and verbose output!** > Note: enable with `--color` for color and `--verbose` for verbose. To open an interactive window with color, use `python - --color` 2. Modular design (plug and play) 3. Good code linting support 4. Register **your own module** to the library. 5. Native Pytorch Output `trojanzoo` and `trojanvision` provides API to generate raw pytorch instances, which makes it flexible to work with native `pytorch` and other 3rd party libraries. > `trojanzoo.datasets.DataSet` can generate `torch.utils.data.Dataset` and `torch.utils.data.DataLoader` > `trojanzoo.models.Model` attribute `_model` is `torch.nn.Module`, attribute `model` is `torch.nn.DataParallel` > Specifically, `trojanvision.datasets.ImageSet` can generate `torchvision.datasets.VisionDataset`, `trojanvision.datasets.ImageFolder` can generate `torchvision.datasets.ImageFolder` 6. Enable pytorch native AMP(Automatic Mixed Precision) with `--amp` for training 7. Flexible Configuration Files 8. Good help information to check arguments. (`-h` or `--help`) 9. Detailed and well-organized `summary()` for each module. ## Installation 1. `pip install trojanzoo` 2. `pip install --upgrade git+https://github.com/ain-soph/trojanzoo.git` 3. **(HIGHLY RECOMMEND)** ``` git clone https://github.com/ain-soph/trojanzoo pip install -e trojanzoo ``` > This could install the github repo as a package but avoid copying files to `site_packages`, so that you can easily keep it updated by doing `git pull`. 4. `docker pull local0state/trojanzoo` or `docker pull ghcr.io/ain-soph/trojanzoo` ## Quick Start You can use the provided [example](https://github.com/ain-soph/trojanzoo/tree/main/examples) scripts to reproduce the evaluation results in our paper
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matched fp:c040517f21393ba2, topic:deep-learning, topic:pytorch
matched fp:c040517f21393ba2, topic:image-classification, desc:image classification, readme:image classification