fdd-defense

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import numpy as np
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from fdd_defense.attackers.base import BaseAttacker
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class PGDAttacker(BaseAttacker):
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    def __init__(
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            self, 
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            model: object, 
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            eps: float,
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            num_steps: int=10,
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        ):
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        super().__init__(model, eps)
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        self.alpha = self.eps / num_steps
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        self.num_steps = num_steps
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    def attack(self, ts, label):
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        super().attack(ts, label)
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        delta = np.zeros_like(ts)
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        for _ in range(self.num_steps):
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            grad = self.model.get_grad(ts + delta, label)
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            delta += self.alpha * np.sign(grad)
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            delta = np.clip(delta, a_min=-self.eps, a_max=self.eps)
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        return ts + delta
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