Awesome

Backdoor Attacks on Vision Transformers

Official Repository of <a href="https://arxiv.org/abs/2206.08477"> ''Backdoor Attacks on Vision Transformers''</a>.

transformer_teaser

Requirements

Python >= 3.7.6
PyTorch >= 1.4
torchvision >= 0.5.0
timm==0.3.2

Dataset creation

We follow the same steps as <a href="https://github.com/UMBCvision/Hidden-Trigger-Backdoor-Attacks"> ''Hidden Trigger Backdoor Attacks''</a> for dataset preparations. We repeat the instructions here for convenience.

python create_imagenet_filelist.py cfg/dataset.cfg

Change ImageNet data source in dataset.cfg
This script partitions the ImageNet train and val data into poison generation, finetune and val to run HTBA attack. Change this for your specific needs.

Configuration file

Please create a separate configuration file for each experiment.
One example is cfg/singlesource_singletarget_1000class_finetune_deit_base/experiment_0001_base.cfg. Create a copy and make desired changes.
The configuration file makes it easy to control all parameters (e.g. poison injection rate, epsilon, patch_size, trigger_ID)

Poison generation

First create directory data/transformer/<EXPERIMENT_ID> and a file in it named source_wnid_list.txt which will contain all the wnids of the source categories for the experiment.

python generate_poison_transformer.py cfg/singlesource_singletarget_1000class_finetune_deit_base/experiment_0001_base.cfg

Finetune

python finetune_transformer.py cfg/singlesource_singletarget_1000class_finetune_deit_base/experiment_0001_base.cfg

Test-time defense

python test_time_defense.py cfg/singlesource_singletarget_1000class_finetune_deit_base/experiment_0001_base.cfg

Data

We have provided the triggers used in our experiments in data/triggers
To reproduce our experiments please use the correct poison injection rates. There might be some variation in numbers depending on the randomness of the ImageNet data split.

License

This project is under the MIT license.

Citation

Please cite us using:

@article{subramanya2022backdoor,
  title={Backdoor Attacks on Vision Transformers},
  author={Subramanya, Akshayvarun and Saha, Aniruddha and Koohpayegani, Soroush Abbasi and Tejankar, Ajinkya and Pirsiavash, Hamed},
  journal={arXiv preprint arXiv:2206.08477},
  year={2022}
}