def __init__(self,
end2end_model: CClassifierEnd2EndMalware,
indexes_to_perturb: list,
iterations: int = 100,
is_debug: bool = False,
random_init: bool = False,
threshold: float = 0.5,
penalty_regularizer: float = 0,
store_checkpoints: int = None):
CAttackEvasion.__init__(
self,
end2end_model,
end2end_model,
)
self.iterations = iterations
self.is_debug = is_debug
self.indexes_to_perturb = indexes_to_perturb
self.confidences_ = []
self.changes_per_iterations_ = []
self.random_init = random_init
self.embedding_size = end2end_model.get_embedding_size()
self.max_input_length = end2end_model.get_input_max_length()
self.invalid_pos = end2end_model.get_embedding_value()
self.embedding_value = end2end_model.get_embedding_value()
self.shift_values = end2end_model.get_is_shifting_values()
self._invalid_value = torch.tensor([np.infty])
self.threshold = threshold
self.penalty_regularizer = penalty_regularizer
self.store_checkpoints = store_checkpoints
def __init__(
self,
problem: CBlackBoxProblem,
is_debug: bool = False,
):
"""
Create and instance of the genetic optimizer.
Parameters
----------
problem : CBlackBoxProblem
The problem to optimize
is_debug : bool, default False
If True, debug prints will be displayed during the attack.
Default is False
"""
CAttackEvasion.__init__(
self,
problem.model_wrapper.classifier,
problem.model_wrapper.classifier,
)
self.problem = problem
self.confidences_ = []
self.fitness_ = []
self.sizes_ = []
self.changes_per_iterations_ = []
self.model_wrapper = problem.model_wrapper
self.is_debug = is_debug
self._original_x = None
self.minimization_result_ = []
self.evolved_problem_ = None
self.stagnation = 5
self.elapsed_time_ = 0
def __init__(self, problem: CBlackBoxProblem, is_debug: bool = False):
CAttackEvasion.__init__(
self,
problem.model_wrapper.classifier,
problem.model_wrapper.classifier,
surrogate_data=CDataset(CArray([[0], [1]]), CArray([0, 1])),
y_target=None,
)
self.problem = problem
self.confidences_ = []
self.changes_per_iterations_ = []
self.model_wrapper = problem.model_wrapper
self.is_debug = is_debug
self._original_x = None
self.minimization_result_ = []
def __init__(
self,
problem: CBlackBoxProblem,
is_debug: bool = False,
):
CAttackEvasion.__init__(
self,
problem.model_wrapper.classifier,
problem.model_wrapper.classifier,
)
self.problem = problem
self.confidences_ = []
self.fitness_ = []
self.sizes_ = []
self.changes_per_iterations_ = []
self.model_wrapper = problem.model_wrapper
self.is_debug = is_debug
self._original_x = None
self.minimization_result_ = []
self.evolved_problem_ = None
self.stagnation = 5