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BTI-DBF: Towards Reliable and Efficient Backdoor Trigger Inversion via Decoupling Benign Features

This the official Pytorch implementation of our paper "Towards Reliable and Efficient Backdoor Trigger Inversion via Decoupling Benign Features", accepted by the ICLR 2024 (Spotlight). This research project is developed based on Python 3 and Pytorch.

Requirements

To install requirements:

pip install -r requirements.txt

Make sure the directory follows:

stealingverification
├── checkpoints
│
├── datasets
│   ├── cifar10
│   └── ...
├── models 
│   
├── results
│   

Pretrained Poisoned Models

You can download the poisoned models which we have pretrained from the following link: checkpoints

Our pretrained poisoned models have the following naming format: {dataset}-{attack}-{model}-target{tlabel}.pt.tar. These models contain model parameters and trigger information. You can use the following code to read checkpoints and easily create backdoor samples:

import torch
from loader import Box

opt = cfg.get_arguments().parse_args()
box = Box(opt)
bd_info1, bd_info2, poisoned_model = box.get_state_dict()
testloader = box.get_dataloader(train="test", batch_size=opt.batch_size, shuffle=False)
for imgs, labels in testloader:
    backdoor_imgs = box.poisoned(imgs, bd_info1, bd_info2)

Pretrain Generator

python pretrain.py --dataset cifar --tlabel 5 --model resnet18 --attack wanet --device cuda:0 --size 32 --num_classes 10 --batch_size 128 --attack_type all2one

Inversion

python btidbf.py --dataset cifar --tlabel 5 --model resnet18 --attack wanet --device cuda:0 --size 32 --num_classes 10 --batch_size 128 --attack_type all2one \
--mround 20 --uround 30 --norm_bound 0.3

Defense

BTI-DBF (U)

python btidbfu.py --dataset cifar --tlabel 5 --model resnet18 --attack wanet --device cuda:0 --size 32 --num_classes 10 --batch_size 128 --attack_type all2one \
--mround 20 --uround 30 --norm_bound 0.3 --ul_round 30 --nround 5

BTI-DBF (P)

python btidbfp.py --dataset cifar --tlabel 5 --model resnet18 --attack wanet --device cuda:0 --size 32 --num_classes 10 --batch_size 128 --attack_type all2one \
--mround 20 --uround 30 --norm_bound 0.3 --pur_round 30 --nround 5 --pur_norm_bound 0.05

Citation

If our work or this repo is useful for your research, please cite our paper as follows:

@inproceedings{xu2024towards,
  title={Towards Reliable and Efficient Backdoor Trigger Inversion via Decoupling Benign Features},
  author={Xu, Xiong and Huang, Kunzhe and Li, Yiming and Qin, Zhan and Ren, Kui},
  booktitle={ICLR},
  year={2024}
}