Poison Ink

This repo is implementation for the accepted paper "Poison Ink: Robust and Invisible Backdoor Attack" (IEEE Transactions on Image Processing (TIP)). [PDF]

Figure 1: The overall pipeline of the proposed Poison Ink, which mainly consists of trigger image generation, backdoor model training and backdoor model attacking.

How to run

(Take VGG19(BN) on CIFAR10 as an example)

1.) Train the injection network (IN) and guidance extrator (GE), simultaneously.

Append --rot, --crop, --flip, and --adv in turn, following the incremental training strategy.

cd trigger_generation

python IN_GE.py --hostname #exp_name --remark #remark yourself

The well-trained IN will be saved at ./chk/#exp_name/#remark yourself/

2.) Generate poisoned image with the well-trained IN.

python trigger_generation.py

You can pollute the target dataset with a pre-defined ratio.

3.) Train the baseline model and the backdoored classifier. Refer to pytorch-classification

cd cls/train

python clean_model.py -a vgg19_bn  

python backdoored_model.py -a vgg19_bn  

Requirements

Python >= 3.6
Pytorch >= 1.1.0

We leverage torchsample to augment tensor in the interference layer, and einops is also a good choice.

Citation

If you find this work useful for your research, please cite

@article{zhang2022poison,
  title={Poison ink: Robust and invisible backdoor attack},
  author={Zhang, Jie and Dongdong, Chen and Huang, Qidong and Liao, Jing and Zhang, Weiming and Feng, Huamin and Hua, Gang and Yu, Nenghai},
  journal={IEEE Transactions on Image Processing},
  volume={31},
  pages={5691--5705},
  year={2022},
  publisher={IEEE}
}

License and Copyright

The project is open source under MIT license (see the LICENSE file).