Source code for attack.inputaware

from .badnet import BadNet
class InputAware(BadNet):

    r"""Input-aware dynamic backdoor attack

        basic sturcture:
            1. config args, save_path, fix random seed
            2. set the device, model, criterion, optimizer, training schedule.
            3. set the clean train data and clean test data
            4. clean train 25 epochs
            5. training with backdoor modification simultaneously
            6. save attack result


        .. code-block:: python

            attack = InputAware()
            attack.attack()

        .. Note::

            @article{nguyen2020input,
            title={Input-aware dynamic backdoor attack},
            author={Nguyen, Tuan Anh and Tran, Anh},
            journal={Advances in Neural Information Processing Systems},
            volume={33},
            pages={3454--3464},
            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
            lr_G (float): learning rate for generator
            lr_C (float): learning rate for classifier
            lr_M (float): learning rate for mask
            C_lr_scheduler (string): learning rate scheduler for classifier
            schedulerG_milestones (list): learning rate scheduler milestones for generator
            schedulerC_milestones (list): learning rate scheduler milestones for classifier
            schedulerM_milestones (list): learning rate scheduler milestones for mask
            schedulerG_lambda (float): learning rate scheduler lambda for generator
            schedulerC_lambda (float): learning rate scheduler lambda for classifier
            schedulerM_lambda (float): learning rate scheduler lambda for mask
            lambda_div (float): lambda_div for diversity loss function
            lambda_norm (float): lambda_norm for norm loss function
            mask_density (float): mask_density for loss function, restrict the mask density
            EPSILON (float): EPSILON for loss function, avoid the denominator to be zero
            clean_train_epochs (int): clean_train_epochs for training epochs before backdoor attack
            **kwargs (optional): Additional attributes.

        """