Source code for captum.robust._core.fgsm

#!/usr/bin/env python3
from typing import Any, Callable, Optional, Tuple, Union

import torch
from captum._utils.common import (
from captum._utils.gradient import (
from captum._utils.typing import TensorOrTupleOfTensorsGeneric
from captum.log import log_usage
from captum.robust._core.perturbation import Perturbation
from torch import Tensor

[docs] class FGSM(Perturbation): r""" Fast Gradient Sign Method is a one-step method that can generate adversarial examples. For non-targeted attack, the formulation is:: x' = x + epsilon * sign(gradient of L(theta, x, y)) For targeted attack on t, the formulation is:: x' = x - epsilon * sign(gradient of L(theta, x, t)) ``L(theta, x, y)`` is the model's loss function with respect to model parameters, inputs and labels. More details on Fast Gradient Sign Method can be found in the original paper: """ def __init__( self, forward_func: Callable, loss_func: Optional[Callable] = None, lower_bound: float = float("-inf"), upper_bound: float = float("inf"), ) -> None: r""" Args: forward_func (Callable): The pytorch model for which the attack is computed. loss_func (Callable, optional): Loss function of which the gradient computed. The loss function should take in outputs of the model and labels, and return a loss tensor. The default loss function is negative log. lower_bound (float, optional): Lower bound of input values. Default: ``float("-inf")`` upper_bound (float, optional): Upper bound of input values. e.g. image pixels must be in the range 0-255 Default: ``float("inf")`` Attributes: bound (Callable): A function that bounds the input values based on given lower_bound and upper_bound. Can be overwritten for custom use cases if necessary. zero_thresh (float): The threshold below which gradient will be treated as zero. Can be modified for custom use cases if necessary. """ super().__init__() self.forward_func = forward_func self.loss_func = loss_func self.bound = lambda x: torch.clamp(x, min=lower_bound, max=upper_bound) self.zero_thresh = 10**-6
[docs] @log_usage() def perturb( self, inputs: TensorOrTupleOfTensorsGeneric, epsilon: float, target: Any, additional_forward_args: Any = None, targeted: bool = False, mask: Optional[TensorOrTupleOfTensorsGeneric] = None, ) -> TensorOrTupleOfTensorsGeneric: r""" This method computes and returns the perturbed input for each input tensor. It supports both targeted and non-targeted attacks. Args: inputs (Tensor or tuple[Tensor, ...]): Input for which adversarial attack is computed. It can be provided as a single tensor or a tuple of multiple tensors. If multiple input tensors are provided, the batch sizes must be aligned across all tensors. epsilon (float): Step size of perturbation. target (Any): True labels of inputs if non-targeted attack is desired. Target class of inputs if targeted attack is desired. Target will be passed to the loss function to compute loss, so the type needs to match the argument type of the loss function. If using the default negative log as loss function, labels should be of type int, tuple, tensor or list. For general 2D outputs, labels can be either: - a single integer or a tensor containing a single integer, which is applied to all input examples - a list of integers or a 1D tensor, with length matching the number of examples in inputs (dim 0). Each integer is applied as the label for the corresponding example. For outputs with > 2 dimensions, labels can be either: - A single tuple, which contains #output_dims - 1 elements. This label index is applied to all examples. - A list of tuples with length equal to the number of examples in inputs (dim 0), and each tuple containing #output_dims - 1 elements. Each tuple is applied as the label for the corresponding example. additional_forward_args (Any, optional): If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. These arguments are provided to forward_func in order following the arguments in inputs. Default: None. targeted (bool, optional): If attack should be targeted. Default: False. mask (Tensor or tuple[Tensor, ...], optional): mask of zeroes and ones that defines which elements within the input tensor(s) are perturbed. This mask must have the same shape and dimensionality as the inputs. If this argument is not provided, all elements will be perturbed. Default: None. Returns: - **perturbed inputs** (*Tensor* or *tuple[Tensor, ...]*): Perturbed input for each input tensor. The perturbed inputs have the same shape and dimensionality as the inputs. If a single tensor is provided as inputs, a single tensor is returned. If a tuple is provided for inputs, a tuple of corresponding sized tensors is returned. """ is_inputs_tuple = _is_tuple(inputs) inputs: Tuple[Tensor, ...] = _format_tensor_into_tuples(inputs) masks: Union[Tuple[int, ...], Tuple[Tensor, ...]] = ( _format_tensor_into_tuples(mask) if (mask is not None) else (1,) * len(inputs) ) gradient_mask = apply_gradient_requirements(inputs) def _forward_with_loss() -> Tensor: additional_inputs = _format_additional_forward_args(additional_forward_args) outputs = self.forward_func( # type: ignore *( (*inputs, *additional_inputs) # type: ignore if additional_inputs is not None else inputs ) ) if self.loss_func is not None: return self.loss_func(outputs, target) else: loss = -torch.log(outputs) return _select_targets(loss, target) grads = compute_gradients(_forward_with_loss, inputs) undo_gradient_requirements(inputs, gradient_mask) perturbed_inputs = self._perturb(inputs, grads, epsilon, targeted, masks) perturbed_inputs = tuple( self.bound(perturbed_inputs[i]) for i in range(len(perturbed_inputs)) ) return _format_output(is_inputs_tuple, perturbed_inputs)
def _perturb( self, inputs: Tuple, grads: Tuple, epsilon: float, targeted: bool, masks: Tuple, ) -> Tuple: r""" A helper function to calculate the perturbed inputs given original inputs, gradient of loss function and epsilon. The calculation is different for targeted v.s. non-targeted as described above. """ multiplier = -1 if targeted else 1 inputs = tuple( torch.where( torch.abs(grad) > self.zero_thresh, inp + multiplier * epsilon * torch.sign(grad) * mask, inp, ) for grad, inp, mask in zip(grads, inputs, masks) ) return inputs