lc.py

'''
Label-Consistent Backdoor Attacks
this script is for lc attack

Note that in our implementation poisoning ratio is ALWAYS defined as
number of poisoning samples / number of all samples instead of
number of poisoning samples / number of samples in target class !!!

link : https://github.com/MadryLab/label-consistent-backdoor-code
The original license is placed at the end of this file.

@article{turner2019labelconsistent,
	title        = {Label-Consistent Backdoor Attacks},
	author       = {Alexander Turner and Dimitris Tsipras and Aleksander Madry},
	journal      = {arXiv preprint arXiv:1912.02771},
	year         = {2019}
}

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



The original license :

MIT License

Copyright (c) 2019 Alexander Turner, Dimitris Tsipras and Aleksander Madry

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

'''

import argparse
import logging
import os
import sys
import torch

import numpy as np

sys.path = ["./"] + sys.path

from attack.badnet import BadNet, add_common_attack_args
from utils.aggregate_block.dataset_and_transform_generate import get_num_classes, get_input_shape
from utils.backdoor_generate_poison_index import generate_poison_index_from_label_transform
from utils.aggregate_block.bd_attack_generate import bd_attack_img_trans_generate, bd_attack_label_trans_generate
from copy import deepcopy
from utils.bd_dataset_v2 import prepro_cls_DatasetBD_v2, dataset_wrapper_with_transform


class LabelConsistent(BadNet):

    def set_bd_args(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser:

        parser = add_common_attack_args(parser)
        parser.add_argument('--attack_train_replace_imgs_path', type=str)
        # parser.add_argument('--attack_test_replace_imgs_path', type=str)
        parser.add_argument("--reduced_amplitude", type=float, )
        parser.add_argument('--bd_yaml_path', type=str, default='./config/attack/lc/default.yaml',
                            help='path for yaml file provide additional default attributes')
        return parser

    def process_args(self, args):

        eps = 16

        args.terminal_info = sys.argv
        args.num_classes = get_num_classes(args.dataset)
        args.input_height, args.input_width, args.input_channel = get_input_shape(args.dataset)
        args.img_size = (args.input_height, args.input_width, args.input_channel)
        args.dataset_path = f"{args.dataset_path}/{args.dataset}"

        if ('attack_train_replace_imgs_path' not in args.__dict__) or (args.attack_train_replace_imgs_path is None):
            args.attack_train_replace_imgs_path = f"./resource/label-consistent/data/{args.model}_{args.dataset}_{eps}_train.npy"
            logging.info(
                f"args.attack_train_replace_imgs_path does not found, so = {args.attack_train_replace_imgs_path}")

        if ('attack_test_replace_imgs_path' not in args.__dict__) or (args.attack_test_replace_imgs_path is None):
            args.attack_test_replace_imgs_path = f"./resource/label-consistent/data/adv_dataset/{args.dataset}_test_v2.npy"
            logging.info(
                f"args.attack_test_replace_imgs_path does not found, so = {args.attack_test_replace_imgs_path}")

        return args

    def stage1_non_training_data_prepare(self):

        logging.info(f"stage1 start")

        assert 'args' in self.__dict__
        args = self.args

        train_dataset_without_transform, \
        train_img_transform, \
        train_label_transform, \
        test_dataset_without_transform, \
        test_img_transform, \
        test_label_transform, \
        clean_train_dataset_with_transform, \
        clean_train_dataset_targets, \
        clean_test_dataset_with_transform, \
        clean_test_dataset_targets \
            = self.benign_prepare()

        train_bd_img_transform, test_bd_img_transform = bd_attack_img_trans_generate(args)
        ### get the backdoor transform on label
        bd_label_transform = bd_attack_label_trans_generate(args)

        ### 4. set the backdoor attack data and backdoor test data
        train_poison_index = generate_poison_index_from_label_transform(
            clean_train_dataset_targets,
            label_transform=bd_label_transform,
            train=True,
            pratio=args.pratio if 'pratio' in args.__dict__ else None,
            p_num=args.p_num if 'p_num' in args.__dict__ else None,
            clean_label=True,
        )

        logging.debug(f"poison train idx is saved")
        torch.save(train_poison_index,
                   args.save_path + '/train_poison_index_list.pickle',
                   )

        ### generate train dataset for backdoor attack
        bd_train_dataset = prepro_cls_DatasetBD_v2(
            deepcopy(train_dataset_without_transform),
            poison_indicator=train_poison_index,
            bd_image_pre_transform=train_bd_img_transform,
            bd_label_pre_transform=bd_label_transform,
            save_folder_path=f"{args.save_path}/bd_train_dataset",
        )

        bd_train_dataset_with_transform = dataset_wrapper_with_transform(
            bd_train_dataset,
            train_img_transform,
            train_label_transform,
        )

        ### decide which img to poison in ASR Test
        test_poison_index = generate_poison_index_from_label_transform(
            clean_test_dataset_targets,
            label_transform=bd_label_transform,
            train=False,
        )

        ### generate test dataset for ASR
        bd_test_dataset = prepro_cls_DatasetBD_v2(
            deepcopy(test_dataset_without_transform),
            poison_indicator=test_poison_index,
            bd_image_pre_transform=test_bd_img_transform,
            bd_label_pre_transform=bd_label_transform,
            save_folder_path=f"{args.save_path}/bd_test_dataset",
        )

        bd_test_dataset.subset(
            np.where(test_poison_index == 1)[0]
        )

        bd_test_dataset_with_transform = dataset_wrapper_with_transform(
            bd_test_dataset,
            test_img_transform,
            test_label_transform,
        )

        self.stage1_results = clean_train_dataset_with_transform, \
                              clean_test_dataset_with_transform, \
                              bd_train_dataset_with_transform, \
                              bd_test_dataset_with_transform


if __name__ == '__main__':
    attack = LabelConsistent()
    parser = argparse.ArgumentParser(description=sys.argv[0])
    parser = attack.set_args(parser)
    parser = attack.set_bd_args(parser)
    args = parser.parse_args()
    attack.add_bd_yaml_to_args(args)
    attack.add_yaml_to_args(args)
    args = attack.process_args(args)
    attack.prepare(args)
    attack.stage1_non_training_data_prepare()
    attack.stage2_training()

"""    
MIT License

Copyright (c) 2019 Alexander Turner, Dimitris Tsipras and Aleksander Madry

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""