from .badnet import BadNet
[docs]class Wanet(BadNet):
r"""WaNet - Imperceptible Warping-based Backdoor Attack
basic sturcture:
1. config args, save_path, fix random seed
2. set the clean train data and clean test data
3. set the device, model, criterion, optimizer, training schedule.
4. set the backdoor warping
5. training with backdoor modification simultaneously
6. save attack result
.. code-block:: python
attack = Wanet()
attack.attack()
.. Note::
@inproceedings{
nguyen2021wanet,
title={WaNet - Imperceptible Warping-based Backdoor Attack},
author={Tuan Anh Nguyen and Anh Tuan Tran},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=eEn8KTtJOx}}
Args:
attack (string): name of attack, use to match the transform and set the saving prefix of path.
attack_target (Int): target class No. in all2one attack
attack_label_trans (str): which type of label modification in backdoor attack
pratio (float): the poison rate in a batch
bd_yaml_path (string): path for yaml file provide additional default attributes
cross_ratio (float): the ratio of cross sample in a batch
random_rotation (int): the max degree of random rotation
random_crop (int): the max padding of random crop
s (int): the strenght of noise grid
k (int): the backdoor warpping size (k,k)
grid_rescale (float): the rescale size for backdoor warpping image values (for example [-1,1] -> [-0.5,0.5])
**kwargs (optional): Additional attributes.
"""