"""
Determine successful in untargeted Classification
----------------------------------------------------
"""
from .classification_goal_function import ClassificationGoalFunction
[docs]class UntargetedClassification(ClassificationGoalFunction):
"""An untargeted attack on classification models which attempts to minimize
the score of the correct label until it is no longer the predicted label.
Args:
target_max_score (float): If set, goal is to reduce model output to
below this score. Otherwise, goal is to change the overall predicted
class.
"""
def __init__(self, *args, target_max_score=None, **kwargs):
self.target_max_score = target_max_score
super().__init__(*args, **kwargs)
def _is_goal_complete(self, model_output, _):
if self.target_max_score:
return model_output[self.ground_truth_output] < self.target_max_score
elif (model_output.numel() == 1) and isinstance(
self.ground_truth_output, float
):
return abs(self.ground_truth_output - model_output.item()) >= 0.5
else:
return model_output.argmax() != self.ground_truth_output
def _get_score(self, model_output, _):
# If the model outputs a single number and the ground truth output is
# a float, we assume that this is a regression task.
if (model_output.numel() == 1) and isinstance(self.ground_truth_output, float):
return abs(model_output.item() - self.ground_truth_output)
else:
return 1 - model_output[self.ground_truth_output]