from .badnet import BadNet
[docs]class Refool(BadNet):
r"""Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
basic structure:
1. config args, save_path, fix random seed
2. set the clean train data and clean test data
3. set the attack img transform and label transform
4. set the backdoor attack data and backdoor test data
5. set the device, model, criterion, optimizer, training schedule.
6. save the attack result for defense
.. code-block:: python
attack = Refool()
attack.attack()
.. Note::
@inproceedings{Liu2020Refool,
title={Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks},
author={Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu},
booktitle={ECCV},
year={2020}}
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
bd_yaml_path (string): path for yaml file provide additional default attributes
r_adv_img_folder_path(float): where the selected r_adv put, used for generate blended imgs
ghost_rate(float): ghost rate for blended imgs
**kwargs (optional): Additional attributes.
"""