from .badnet import BadNet
[docs]class CTRL(BadNet):
r"""An Embarrassingly Simple Backdoor Attack on Self-supervised Learning
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. attack or use the model to do finetune with 5% clean data
7. save the attack result for defense
.. code-block:: python
attack = ctrl()
attack.attack()
.. Note::
@InProceedings{Li_2023_ICCV,
author = {Li, Changjiang and Pang, Ren and Xi, Zhaohan and Du, Tianyu and Ji, Shouling and Yao, Yuan and Wang, Ting},
title = {An Embarrassingly Simple Backdoor Attack on Self-supervised Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {4367-4378}}
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
use_dct(bool): use dct in trigger generation or not
use_yuv(bool): transform into yuv space in trigger generation or not
trigger_channels(int): which channel(s) you want to do trigger injection
add_patch_info(int): (x,y,width,height,magnitude,mode)
add_patch_info_train(int): (x,y,width,height,magnitude,mode) used only if you want nonsymmetric trigger injection in train and test
add_patch_info_test(int): (x,y,width,height,magnitude,mode) used only if you want nonsymmetric trigger injection in train and test
**kwargs (optional): Additional attributes.
"""