from .badnet import BadNet
[docs]class Blind(BadNet):
r'''Blind Backdoors in Deep Learning Models
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. use the designed blind_loss to train a poisoned model
7. save the attack result for defense
.. code-block:: python
attack = Blind()
attack.attack()
.. Note::
@inproceedings {bagdasaryan2020blind,
author = {Eugene Bagdasaryan and Vitaly Shmatikov},
title = {Blind Backdoors in Deep Learning Models},
booktitle = {30th {USENIX} Security Symposium ({USENIX} Security 21)},
year = {2021},
isbn = {978-1-939133-24-3},
pages = {1505--1521},
url = {https://www.usenix.org/conference/usenixsecurity21/presentation/bagdasaryan},
publisher = {USENIX Association},
month = aug,}
Args:
attack (string): name of attack, use to match the transform and set the saving prefix of path.
attack_target (int): target class in all2one attack
attack_label_trans (string): which type of label modification in backdoor attack
bd_yaml_path (string): path of yaml file to load backdoor attack config
weight_loss_balance_mode (string): weight loss balance mode (eg. "fixed")
mgda_normalize (string): mgda normalize mode (eg. "l2", "loss+")
fix_scale_normal_weight (float): fix scale normal weight
fix_scale_backdoor_weight (float): fix scale backdoor weight
batch_history_len (int): len of tracking history to compute when training is stable, so we start to attack
backdoor_batch_loss_threshold (float): threshold of backdoor batch loss to compute when training is stable, so we start to attack
**kwargs (optional): Additional attributes.
'''