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import torch.nn.functional as F
from torch.utils.data import Dataset
from typing import Union
import warnings
from src.attacks.offline.trainable import TrainableAttack
from src.attacks.offline.perturbation.perturbation import Perturbation
from src.attacks.offline.perturbation.kenansville import KenansvillePerturbation
from src.pipelines.pipeline import Pipeline
from src.loss.adversarial import AdversarialLoss
from src.loss.auxiliary import AuxiliaryLoss
from src.utils.writer import Writer
################################################################################
# Untargeted, black-box signal-processing attack
################################################################################
class KenansvilleAttack(TrainableAttack):
"""
Perturb inputs by removing frequency content.
"""
def __init__(self,
pipeline: Pipeline,
adv_loss: AdversarialLoss,
threshold_db_low: float = 1.0,
threshold_db_high: float = 100.0,
step_size: float = 10.0,
search: str = 'bisection',
min_success_rate: float = 0.9,
win_type: str = 'hann',
win_length: int = 2048,
**kwargs
):
"""
Untargeted black-box spectral-bin-removal attack proposed by Abdullah
et al. (https://arxiv.org/abs/1910.05262). Code adapted from
https://bit.ly/31K4Efy.
:param pipeline: a Pipeline object
:param adv_loss: an AdversarialLoss object; must be untargeted
:param aux_loss: an optional AuxiliaryLoss object
:param threshold_db: energy threshold relative to spectral peak energy;
frequency bins below threshold are removed
:param max_iter: iterations to search for optimal threshold. If nonzero,
search for highest (least perceptible) threshold value
such that attack achieves 100% untargeted success
against given pipeline. Otherwise, use given threshold
:param min_success_rate: minimum acceptable untargeted success rate when
optimizing threshold
:param win_type: window type; must be one of 'rectangular' or 'hann'.
For Hann window, audio is framed with 50% overlap
:param frame_len: frame length in samples
"""
self.threshold_db_low = threshold_db_low
self.threshold_db_high = threshold_db_high
self.step_size = step_size
self.search = search
self.min_success_rate = min_success_rate
super().__init__(
pipeline=pipeline,
adv_loss=adv_loss,
perturbation=KenansvillePerturbation(
threshold_db=threshold_db_low,
win_type=win_type,
win_length=win_length
),
**kwargs
)
@torch.no_grad()
def train(self,
x_train: torch.Tensor = None,
y_train: torch.Tensor = None,
data_train: Dataset = None,
x_val: torch.Tensor = None,
y_val: torch.Tensor = None,
data_val: Dataset = None,
*args,
**kwargs
):
loader_train, loader_val = self._prepare_data(
x_train,
y_train,
data_train,
x_val,
y_val,
data_val)
# match devices and set reference if necessary
ref_batch = next(iter(loader_train))
if isinstance(ref_batch, tuple):
x_ref = ref_batch[0]
warnings.warn('Warning: provided dataset yields batches in tuple '
'format; the first two tensors of each batch will be '
'interpreted as inputs and targets, respectively, '
'and any remaining tensors will be ignored. To pass '
'additional named tensor arguments, use a dictionary '
'batch format with keys `x` and `y` for inputs and '
'targets, respectively.')
elif isinstance(ref_batch, dict):
x_ref = ref_batch['x']
else:
x_ref = ref_batch
if hasattr(self.perturbation, "set_reference"):
try:
self.perturbation.set_reference(
x_ref.to(self.pipeline.device))
except AttributeError:
pass
# enumerate possible SNR values for search
threshold_values = torch.arange(
self.threshold_db_low, self.threshold_db_high, self.step_size)
# track iterations
self._iter_id = 0
self._batch_id = 0
self._epoch_id = 0
# avoid unnecessary search
if self.threshold_db_low == self.threshold_db_high \
or len(threshold_values) < 2 \
or self.search in ['none', None]:
self.perturbation.set_threshold(self.threshold_db_low)
else:
# find best threshold via search
i_min = 0
i_max = len(threshold_values)
threshold_best = self.threshold_db_low
# perform bisection search for maximum SNR value which achieves
# minimum success threshold
if self.search == 'bisection':
while i_min < i_max:
# determine midpoint index
i_mid = (i_min + i_max) // 2
threshold = threshold_values[i_mid]
# set threshold
self.perturbation.set_threshold(threshold)
# compute success rate over training data at each candidate
# threshold level
successes = 0
n = 0
self._batch_id = 0
for batch in loader_train:
if isinstance(batch, dict):
x, y = batch['x'], batch['y']
else:
x, y, *_ = batch
x = x.to(self.pipeline.device)
y = y.to(self.pipeline.device)
n += len(x)
x_adv = self.perturbation(x)
outputs = self.pipeline(x_adv)
adv_scores = self.adv_loss(outputs, y)
adv_loss = adv_scores.mean()
batch_successes = (1.0 * self._compute_success_array(
x, y, x_adv)).sum().item()
successes += batch_successes
self._log_step(
x,
x_adv,
y,
adv_loss,
success_rate=batch_successes/len(x)
)
self._batch_id += 1
self._iter_id += 1
success_rate = successes / n
if success_rate >= self.min_success_rate:
threshold_best = threshold
i_min = i_mid + 1
else:
i_max = i_mid
# perform linear search for SNR level
elif self.search == 'linear':
for threshold in threshold_values:
# set threshold
self.perturbation.set_threshold(threshold)
# compute success rate over training data at each candidate
# threshold level
successes = 0
n = 0
self._batch_id = 0
for batch in loader_train:
if isinstance(batch, dict):
x, y = batch['x'], batch['y']
else:
x, y, *_ = batch
x = x.to(self.pipeline.device)
y = y.to(self.pipeline.device)
n += len(x)
x_adv = self.perturbation(x)
outputs = self.pipeline(x_adv)
adv_scores = self.adv_loss(outputs, y)
adv_loss = adv_scores.mean()
batch_successes = (1.0 * self._compute_success_array(
x, y, x_adv)).sum().item()
successes += batch_successes
self._log_step(
x,
x_adv,
y,
adv_loss,
success_rate=batch_successes/len(x)
)
self._batch_id += 1
self._iter_id += 1
success_rate = successes / n
if success_rate >= self.min_success_rate:
threshold_best = threshold
else:
raise ValueError(f'Invalid search method {self.search}')
# set final SNR value
self.perturbation.set_threshold(threshold_best)
# perform validation
adv_scores = []
aux_scores = []
det_scores = []
success_indicators = []
detection_indicators = []
self.perturbation.eval()
for batch_id, batch in enumerate(loader_val):
# randomize simulation for each validation batch
self.pipeline.sample_params()
if isinstance(batch, dict):
x_orig, targets = batch['x'], batch['y']
else:
x_orig, targets, *_ = batch
n_batch = x_orig.shape[0]
x_orig = x_orig.to(self.pipeline.device)
targets = targets.to(self.pipeline.device)
# set reference for auxiliary loss
self._set_loss_reference(x_orig)
with torch.no_grad():
# compute adversarial loss
x_adv = self._evaluate_batch(x_orig, targets)
outputs = self.pipeline(x_adv)
adv_scores.append(self.adv_loss(outputs, targets).flatten())
# compute adversarial success rate
success_indicators.append(
1.0 * self._compute_success_array(
x_orig, targets, x_adv
).flatten())
# compute defense loss and detection indicators
def_results = self.pipeline.detect(x_adv)
detection_indicators.append(1.0 * def_results[0].flatten())
det_scores.append(def_results[1].flatten())
# compute auxiliary loss
if self.aux_loss is not None:
aux_scores.append(
self._compute_aux_loss(x_adv).flatten())
else:
aux_scores.append(torch.zeros(n_batch))
tag = f'{self.__class__.__name__}-' \
f'{self.aux_loss.__class__.__name__}'
if self.writer is not None:
with self.writer.force_logging():
# adversarial loss value
self.writer.log_scalar(
torch.cat(adv_scores, dim=0).mean(),
f"{tag}/adversarial-loss-val",
global_step=self._iter_id
)
# detector loss value
self.writer.log_scalar(
torch.cat(det_scores, dim=0).mean(),
f"{tag}/detector-loss-val",
global_step=self._iter_id
)
# auxiliary loss value
self.writer.log_scalar(
torch.cat(aux_scores, dim=0).mean(),
f"{tag}/auxiliary-loss-val",
global_step=self._iter_id
)
# adversarial success rate
self.writer.log_scalar(
torch.cat(success_indicators, dim=0).mean(),
f"{tag}/success-rate-val",
global_step=self._iter_id
)
# adversarial detection rate
self.writer.log_scalar(
torch.cat(detection_indicators, dim=0).mean(),
f"{tag}/detection-rate-val",
global_step=self._iter_id
)
# freeze model parameters
self.perturbation.eval()
for p in self.perturbation.parameters():
p.requires_grad = False
# save model/perturbation
self._checkpoint()
def _evaluate_batch(self,
x: torch.Tensor,
y: torch.Tensor,
**kwargs
):
"""
Remove low-energy frequency content from inputs.
"""
# require batch dimension
assert x.ndim >= 2
return self.perturbation(x)
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