fgsm

FGSM

Bases: TensorOp

Create an adversarial sample from input data using the Fast Gradient Sign Method.

See https://arxiv.org/abs/1412.6572 for an explanation of adversarial attacks.

Parameters:

Name	Type	Description	Default
data	str	Key of the input to be attacked.	required
loss	str	Key of the loss value to use for gradient computation.	required
outputs	str	The key under which to save the output.	required
epsilon	float	The strength of the perturbation to use in the attack.	0.01
clip_low	Optional[float]	a minimum value to clip the output by (defaults to min value of data when set to None).	None
clip_high	Optional[float]	a maximum value to clip the output by (defaults to max value of data when set to None).	None
mode	Union[None, str, Iterable[str]]	What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument like "!infer" or "!train".	None
ds_id	Union[None, str, Iterable[str]]	What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all ds_ids except for a particular one, you can pass an argument like "!ds1".	None

Source code in fastestimator/fastestimator/op/tensorop/gradient/fgsm.py

@traceable()
class FGSM(TensorOp):
    """Create an adversarial sample from input data using the Fast Gradient Sign Method.

    See https://arxiv.org/abs/1412.6572 for an explanation of adversarial attacks.

    Args:
        data: Key of the input to be attacked.
        loss: Key of the loss value to use for gradient computation.
        outputs: The key under which to save the output.
        epsilon: The strength of the perturbation to use in the attack.
        clip_low: a minimum value to clip the output by (defaults to min value of data when set to None).
        clip_high: a maximum value to clip the output by (defaults to max value of data when set to None).
        mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
            regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
            like "!infer" or "!train".
        ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
            ds_ids except for a particular one, you can pass an argument like "!ds1".
    """
    def __init__(self,
                 data: str,
                 loss: str,
                 outputs: str,
                 epsilon: float = 0.01,
                 clip_low: Optional[float] = None,
                 clip_high: Optional[float] = None,
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None):
        super().__init__(inputs=[data, loss], outputs=outputs, mode=mode, ds_id=ds_id)
        self.epsilon = epsilon
        self.clip_low = clip_low
        self.clip_high = clip_high
        self.retain_graph = True

    def fe_retain_graph(self, retain: Optional[bool] = None) -> Optional[bool]:
        if retain is not None:
            self.retain_graph = retain
        return self.retain_graph

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> Tensor:
        data, loss = data
        grad = get_gradient(target=loss, sources=data, tape=state['tape'], retain_graph=self.retain_graph)
        adverse_data = clip_by_value(data + self.epsilon * sign(grad),
                                     min_value=self.clip_low or reduce_min(data),
                                     max_value=self.clip_high or reduce_max(data))
        return adverse_data