Source code for captum.robust._core.pgd

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

import torch
import torch.nn.functional as F
from captum._utils.common import _format_output, _format_tensor_into_tuples, _is_tuple
from captum._utils.typing import TensorOrTupleOfTensorsGeneric
from captum.log import log_usage
from captum.robust._core.fgsm import FGSM
from captum.robust._core.perturbation import Perturbation
from torch import Tensor

[docs] class PGD(Perturbation): r""" Projected Gradient Descent is an iterative version of the one-step attack FGSM that can generate adversarial examples. It takes multiple gradient steps to search for an adversarial perturbation within the desired neighbor ball around the original inputs. In a non-targeted attack, the formulation is:: x_0 = x x_(t+1) = Clip_r(x_t + alpha * sign(gradient of L(theta, x, t))) where Clip denotes the function that projects its argument to the r-neighbor ball around x so that the perturbation will be bounded. Alpha is the step size. L(theta, x, y) is the model's loss function with respect to model parameters, inputs and targets. In a targeted attack, the formulation is similar:: x_0 = x x_(t+1) = Clip_r(x_t - alpha * sign(gradient of L(theta, x, t))) More details on Projected Gradient Descent 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 the loss for each input 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. """ super().__init__() self.forward_func = forward_func self.fgsm = FGSM(forward_func, loss_func) self.bound = lambda x: torch.clamp(x, min=lower_bound, max=upper_bound)
[docs] @log_usage() def perturb( self, inputs: TensorOrTupleOfTensorsGeneric, radius: float, step_size: float, step_num: int, target: Any, additional_forward_args: Any = None, targeted: bool = False, random_start: bool = False, norm: str = "Linf", 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. radius (float): Radius of the neighbor ball centered around inputs. The perturbation should be within this range. step_size (float): Step size of each gradient step. step_num (int): Step numbers. It usually guarantees that the perturbation can reach the border. 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`` random_start (bool, optional): If a random initialization is added to inputs. Default: ``False`` norm (str, optional): Specifies the norm to calculate distance from original inputs: ``Linf`` | ``L2``. Default: ``Linf`` 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 are 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. """ def _clip(inputs: Tensor, outputs: Tensor) -> Tensor: diff = outputs - inputs if norm == "Linf": return inputs + torch.clamp(diff, -radius, radius) elif norm == "L2": return inputs + torch.renorm(diff, 2, 0, radius) else: raise AssertionError("Norm constraint must be L2 or Linf.") is_inputs_tuple = _is_tuple(inputs) formatted_inputs = _format_tensor_into_tuples(inputs) formatted_masks: Union[Tuple[int, ...], Tuple[Tensor, ...]] = ( _format_tensor_into_tuples(mask) if (mask is not None) else (1,) * len(formatted_inputs) ) perturbed_inputs = formatted_inputs if random_start: perturbed_inputs = tuple( self.bound( self._random_point( formatted_inputs[i], radius, norm, formatted_masks[i] ) ) for i in range(len(formatted_inputs)) ) for _i in range(step_num): perturbed_inputs = self.fgsm.perturb( perturbed_inputs, step_size, target, additional_forward_args, targeted, formatted_masks, ) perturbed_inputs = tuple( _clip(formatted_inputs[j], perturbed_inputs[j]) for j in range(len(perturbed_inputs)) ) # Detaching inputs to avoid dependency of gradient between steps perturbed_inputs = tuple( self.bound(perturbed_inputs[j]).detach() for j in range(len(perturbed_inputs)) ) return _format_output(is_inputs_tuple, perturbed_inputs)
def _random_point( self, center: Tensor, radius: float, norm: str, mask: Union[Tensor, int] ) -> Tensor: r""" A helper function that returns a uniform random point within the ball with the given center and radius. Norm should be either L2 or Linf. """ if norm == "L2": u = torch.randn_like(center) unit_u = F.normalize(u.view(u.size(0), -1)).view(u.size()) d = torch.numel(center[0]) r = (torch.rand(u.size(0)) ** (1.0 / d)) * radius r = r[(...,) + (None,) * (r.dim() - 1)] x = r * unit_u return center + (x * mask) elif norm == "Linf": x = torch.rand_like(center) * radius * 2 - radius return center + (x * mask) else: raise AssertionError("Norm constraint must be L2 or Linf.")