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