def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
super(TrainDecoderClassifierAugmented, self).__init__(args)
self.train_statistics = numpy.zeros((0, 8))
self.test_statistics = numpy.zeros((0, 7))
def attack(self):
"""
Test the model.
"""
assert self.model is not None
assert self.model.classifier.training is False
#assert self.model.decoder.training is False
batch_size = 1
objective = self.objective_class()
num_batches = int(math.ceil(self.args.max_samples / batch_size))
self.data = numpy.zeros((self.args.max_samples, 200, 2))
for i in range(num_batches):
i_start = i * batch_size
i_end = min((i + 1) * batch_size, self.args.max_samples)
batch_classes = common.torch.as_variable(
self.test_codes[i_start:i_end], self.args.use_gpu)
batch_theta = common.torch.as_variable(
numpy.zeros((batch_size, self.args.latent_space_size),
dtype=numpy.float32), self.args.use_gpu)
objective = self.objective_class()
self.model.decoder.set_code(batch_classes)
reference_logits = self.model.forward(batch_theta)
attack = self.setup_attack(batch_theta, batch_classes)
success, perturbations, _, _, _ = attack.run(objective)
steps = numpy.linspace(-1, 5, 200)
log('[Visualization] %d class=%d, predicted=%d' %
(i, batch_classes[0], torch.max(reference_logits, dim=1)[1]))
log('[Visualization] %d success=%d' % (i, success[0]))
for s in range(len(steps)):
step = steps[s]
batch_theta = common.torch.as_variable(
numpy.expand_dims(step *
perturbations[0].astype(numpy.float32),
axis=0), self.args.use_gpu)
output_images = self.model.decoder(batch_theta)
output_logits = self.model.classifier(output_images)
f = objective.f(output_logits, reference_logits, batch_classes)
#from matplotlib import pyplot
#pyplot.imshow(output_images[0, 0].cpu().detach().numpy())
#pyplot.show()
self.data[i, s, 0] = batch_theta[:, 5]
self.data[i, s, 1] = f.item()
log('[Visualization] %d step=%g, f=%g' % (i, step, f))
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
super(TrainLearnedDecoderClassifierAdversarially2, self).__init__(args)
self.train_statistics = numpy.zeros((0, 16))
self.test_statistics = numpy.zeros((0, 15))
def attack(self):
"""
Test the model.
"""
assert self.model is not None
assert self.model.classifier.training is False
#assert self.model.decoder.training is False
batch_size = 1
objective = self.objective_class()
num_batches = int(math.ceil(self.args.max_samples / batch_size))
self.data = numpy.zeros((self.args.max_samples, 200, 2))
for i in range(num_batches):
i_start = i * batch_size
i_end = min((i + 1) * batch_size, self.args.max_samples)
batch_classes = common.torch.as_variable(
self.test_codes[i_start:i_end], self.args.use_gpu)
batch_images = common.torch.as_variable(
numpy.expand_dims(self.test_images[i_start:i_end], axis=0),
self.args.use_gpu)
batch_theta = common.torch.as_variable(
numpy.zeros(
(batch_size, self.args.N_theta)).astype(numpy.float32),
self.args.use_gpu)
objective = self.objective_class()
self.model.decoder.set_image(batch_images)
reference_logits = self.model.forward(batch_theta)
log('[Visualization] %d class=%d, predicted=%d' %
(i, batch_classes[0], torch.max(reference_logits, dim=1)[1]))
rotations = numpy.linspace(self.min_bound[5], self.max_bound[5],
200)
for r in range(len(rotations)):
rotation = rotations[r]
batch_theta[:, 5] = rotation
output_images = self.model.decoder(batch_theta)
output_logits = self.model.classifier(output_images)
f = objective.f(output_logits, reference_logits, batch_classes)
#from matplotlib import pyplot
#pyplot.imshow(output_images[0, 0].cpu().numpy())
#pyplot.show()
self.data[i, r, 0] = batch_theta[:, 5]
self.data[i, r, 1] = f.item()
log('[Visualization] %d rotation=%g, f=%g' % (i, rotation, f))
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
super(TrainClassifierAdversarially, self).__init__(args)
self.train_statistics = numpy.zeros((0, 11))
self.test_statistics = numpy.zeros((0, 10))
self.attack_class = None
""" (attacks.UntargetedAttack) Attack to use (as class). """
self.objective_class = None
""" (attacks.UntargetedObjective) Objective to use (as class). """
self.norm = None
""" (float) Attack norm for data augmentation. """
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
super(TrainLearnedDecoderClassifierAdversarially, self).__init__(args)
self.train_statistics = numpy.zeros((0, 11))
self.test_statistics = numpy.zeros((0, 10))
self.train_theta = None
""" (numpy.ndarray) Training transformation parameters. """
self.test_theta = None
""" (numpy.ndarray) Testing transformation parameters. """
self.decoder = None
""" (Decoder) Decoder. """
self.decoder_classifier = None
""" (DecoderClassifier) Model to attack. """
def test(self):
"""
Test classifier to identify valid samples to attack.
"""
num_batches = int(
math.ceil(self.test_images.shape[0] / self.args.batch_size))
for b in range(num_batches):
b_start = b * self.args.batch_size
b_end = min((b + 1) * self.args.batch_size,
self.test_images.shape[0])
batch_classes = common.torch.as_variable(
self.test_codes[b_start:b_end], self.args.use_gpu)
batch_images = common.torch.as_variable(
self.test_images[b_start:b_end], self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
batch_theta = common.torch.as_variable(
numpy.zeros((b_end - b_start, self.args.N_theta),
dtype=numpy.float32), self.args.use_gpu)
if self.args.N_theta > 4:
batch_theta[:, 4] = 1
self.model.decoder.set_image(batch_images)
output_classes = self.model(batch_theta)
values, indices = torch.max(torch.nn.functional.softmax(
output_classes, dim=1),
dim=1)
errors = torch.abs(indices - batch_classes)
self.accuracy = common.numpy.concatenate(self.accuracy,
errors.data.cpu().numpy())
if b % 100 == 0:
log('[Attack] computing accuracy %d' % b)
self.accuracy = self.accuracy == 0
utils.write_hdf5(self.args.accuracy_file, self.accuracy)
log('[Attack] wrote %s' % self.args.accuracy_file)
accuracy = numpy.sum(self.accuracy) / float(self.accuracy.shape[0])
log('[Attack] accuracy %g' % accuracy)
accuracy = numpy.sum(self.accuracy[:self.args.max_samples]) / float(
self.args.max_samples)
log('[Attack] accuracy on %d samples %g' %
(self.args.max_samples, accuracy))
def compute_local_pca(self):
"""
Compute PCA.
"""
success = numpy.logical_and(self.success >= 0, self.accuracy)
log('[Detection] %d valid attacked samples' % numpy.sum(success))
nearest_neighbor_images = self.nearest_neighbor_images.reshape(self.nearest_neighbor_images.shape[0], -1)
nearest_neighbor_images = nearest_neighbor_images[:self.args.n_fit]
perturbations = self.perturbations.reshape(self.perturbations.shape[0], -1)
test_images = self.test_images.reshape(self.test_images.shape[0], -1)
pure_perturbations = perturbations - test_images
nearest_neighbors_indices = self.compute_nearest_neighbors(perturbations)
self.distances['true'] = numpy.zeros((success.shape[0]))
self.distances['test'] = numpy.zeros((success.shape[0]))
self.distances['perturbation'] = numpy.zeros((success.shape[0]))
self.angles['true'] = numpy.zeros((success.shape[0]))
self.angles['test'] = numpy.zeros((success.shape[0]))
self.angles['perturbation'] = numpy.zeros((success.shape[0]))
for n in range(pure_perturbations.shape[0]):
if success[n]:
nearest_neighbors = nearest_neighbor_images[nearest_neighbors_indices[n, :]]
nearest_neighbors = numpy.concatenate((nearest_neighbors, test_images[n].reshape(1, -1)), axis=0)
pca = sklearn.decomposition.IncrementalPCA(n_components=self.args.n_pca)
pca.fit(nearest_neighbors)
reconstructed_test_images = pca.inverse_transform(pca.transform(test_images[n].reshape(1, -1)))
reconstructed_perturbations = pca.inverse_transform(pca.transform(perturbations[n].reshape(1, -1)))
reconstructed_pure_perturbations = pca.inverse_transform(pca.transform(pure_perturbations[n].reshape(1, -1)))
self.distances['test'][n] = numpy.average(numpy.multiply(reconstructed_test_images - test_images[n], reconstructed_test_images - test_images[n]), axis=1)
self.distances['perturbation'][n] = numpy.average(numpy.multiply(reconstructed_perturbations - perturbations[n], reconstructed_perturbations - perturbations[n]), axis=1)
self.distances['true'][n] = numpy.average(numpy.multiply(reconstructed_pure_perturbations - pure_perturbations[n], reconstructed_pure_perturbations - pure_perturbations[n]), axis=1)
self.angles['test'][n] = numpy.rad2deg(common.numpy.angles(reconstructed_test_images.T, test_images[n].T))
self.angles['perturbation'][n] = numpy.rad2deg(common.numpy.angles(reconstructed_perturbations.T, perturbations[n].T))
self.angles['true'][n] = numpy.rad2deg(common.numpy.angles(reconstructed_pure_perturbations.T, pure_perturbations[n].T))
log('[Detection] %d: true distance=%g angle=%g' % (n, self.distances['true'][n], self.angles['true'][n]))
log('[Detection] %d: perturbation distance=%g angle=%g' % (n, self.distances['perturbation'][n], self.angles['perturbation'][n]))
log('[Detection] %d: test distance=%g angle=%g' % (n, self.distances['test'][n], self.angles['test'][n]))
self.distances['test'] = self.distances['test'][success]
self.distances['perturbation'] = self.distances['perturbation'][success]
self.distances['true'] = self.distances['true'][success]
def compute_statistics(self):
"""
Compute statistics based on distances.
"""
num_attempts = self.perturbations.shape[0]
perturbations = numpy.swapaxes(self.perturbations, 0, 1)
perturbations = perturbations.reshape(
(perturbations.shape[0] * perturbations.shape[1],
perturbations.shape[2]))
success = numpy.swapaxes(self.success, 0, 1)
success = success.reshape((success.shape[0] * success.shape[1]))
probabilities = numpy.swapaxes(self.probabilities, 0, 1)
probabilities = probabilities.reshape(
(probabilities.shape[0] * probabilities.shape[1], -1))
confidences = numpy.max(probabilities, 1)
perturbation_probabilities = self.test_probabilities[:self.success.
shape[1]]
perturbation_probabilities = numpy.repeat(perturbation_probabilities,
num_attempts,
axis=0)
perturbation_confidences = numpy.max(perturbation_probabilities, 1)
probability_ratios = confidences / perturbation_confidences
raw_overall_success = success >= 0
log('[Testing] %d valid attacks' % numpy.sum(raw_overall_success))
# For off-manifold attacks this should not happen, but save is save.
if not numpy.any(raw_overall_success):
for type in [
'raw_success', 'raw_iteration', 'raw_roc',
'raw_confidence_weighted_success', 'raw_confidence',
'raw_ratios'
]:
self.results[type] = 0
if self.args.results_file:
utils.write_pickle(self.args.results_file, self.results)
log('[Testing] wrote %s' % self.args.results_file)
log('[Testing] no successful attacks found, no plots')
return
#
# We compute some simple statistics:
# - raw success rate: fraction of successful attack without considering epsilon
# - corrected success rate: fraction of successful attacks within epsilon-ball
# - raw average perturbation: average distance to original samples (for successful attacks)
# - corrected average perturbation: average distance to original samples for perturbations
# within epsilon-ball (for successful attacks).
# These statistics can also be computed per class.
# And these statistics are computed with respect to three norms.
if self.args.plot_directory and utils.display():
iterations = success[raw_overall_success]
x = numpy.arange(numpy.max(iterations) + 1)
y = numpy.bincount(iterations)
plot_file = os.path.join(self.args.plot_directory, 'iterations')
plot.bar(plot_file,
x,
y,
title='Distribution of Iterations of Successful Attacks',
xlabel='Number of Iterations',
ylabel='Count')
log('[Testing] wrote %s' % plot_file)
plot_file = os.path.join(self.args.plot_directory, 'probabilities')
plot.histogram(plot_file, confidences[raw_overall_success], 50)
log('[Testing] wrote %s' % plot_file)
plot_file = os.path.join(self.args.plot_directory,
'probability_ratios')
plot.histogram(plot_file, probability_ratios, 50)
log('[Testing] wrote %s' % plot_file)
plot_file = os.path.join(self.args.plot_directory,
'test_probabilities')
plot.histogram(
plot_file, self.test_probabilities[
numpy.arange(self.test_probabilities.shape[0]),
self.test_codes], 50)
log('[Testing] wrote %s' % plot_file)
y_true = numpy.concatenate(
(numpy.zeros(confidences.shape[0]),
numpy.ones(perturbation_confidences.shape[0])))
y_score = numpy.concatenate((confidences, perturbation_confidences))
roc_auc_score = sklearn.metrics.roc_auc_score(y_true, y_score)
self.results['raw_roc'] = roc_auc_score
self.results['raw_confidence_weighted_success'] = numpy.sum(
confidences[raw_overall_success]) / numpy.sum(
perturbation_confidences)
self.results['raw_confidence'] = numpy.mean(
probabilities[raw_overall_success])
self.results['raw_ratios'] = numpy.mean(
probability_ratios[raw_overall_success])
self.results['raw_success'] = numpy.sum(
raw_overall_success) / success.shape[0]
self.results['raw_iteration'] = numpy.average(
success[raw_overall_success])
if self.args.results_file:
utils.write_pickle(self.args.results_file, self.results)
log('[Testing] wrote %s' % self.args.results_file)
def compute_statistics(self):
"""
Compute statistics based on distances.
"""
# That's the basis for all computation as we only want to consider successful attacks
# on test samples that were correctly classified.
raw_overall_success = numpy.logical_and(self.success >= 0, self.accuracy)
# Important check, for on-manifold attack this will happen if the manifold is small and the model very accurate!
if not numpy.any(raw_overall_success):
for n in range(len(self.norms)):
for type in ['raw_success', 'raw_iteration', 'raw_average', 'raw_image']:
self.results[n][type] = 0
for type in ['raw_class_success', 'raw_class_average', 'raw_class_image']:
self.results[n][type] = numpy.zeros((self.N_class))
if self.args.results_file:
utils.write_pickle(self.args.results_file, self.results)
log('[Testing] wrote %s' % self.args.results_file)
return
#
# Compute nearest neighbor statistics in image space.
#
if self.args.plot_directory and self.args.plot_manifolds and utils.display():
log('[Testing] computing nearest neighbor ...')
nearest_neighbors_indices = self.compute_nearest_neighbors(self.perturbation_images[raw_overall_success])
pure_perturbations = self.test_images[raw_overall_success] - self.perturbation_images[raw_overall_success]
pure_perturbations_norm = numpy.linalg.norm(pure_perturbations, ord=2, axis=1)
for k in range(10):
direction = self.perturbation_images[raw_overall_success] - self.train_images[nearest_neighbors_indices[:, k]]
direction_norm = numpy.linalg.norm(direction, ord=2, axis=1)
dot_products = numpy.einsum('ij,ij->i', direction, pure_perturbations)
dot_product_norms = numpy.multiply(pure_perturbations_norm, direction_norm)
dot_products, dot_product_norms = dot_products[dot_product_norms > 10**-8], dot_product_norms[dot_product_norms > 10**-8]
dot_products /= dot_product_norms
dot_products = numpy.degrees(numpy.arccos(dot_products))
# matplotlib's hsitogram plots give weird error if there are NaN values, so simple check:
if dot_products.shape[0] > 0 and not numpy.any(dot_products != dot_products):
plot_file = os.path.join(self.args.plot_directory, 'dot_products_nn%d' % k)
plot.histogram(plot_file, dot_products, 100, xmin=numpy.min(dot_products), xmax=numpy.max(dot_products),
title='Dot Products Between Adversarial Perturbations and Direction to Nearest Neighbor %d' % k,
xlabel='Dot Product', ylabel='Count')
log('[Testing] wrote %s' % plot_file)
#
# We compute some simple statistics:
# - raw success rate: fraction of successful attack without considering epsilon
# - corrected success rate: fraction of successful attacks within epsilon-ball
# - raw average perturbation: average distance to original samples (for successful attacks)
# - corrected average perturbation: average distance to original samples for perturbations
# within epsilon-ball (for successful attacks).
# These statistics can also be computed per class.
# And these statistics are computed with respect to three norms.
if self.args.plot_directory and utils.display():
iterations = self.success[raw_overall_success]
x = numpy.arange(numpy.max(iterations) + 1)
y = numpy.bincount(iterations)
plot_file = os.path.join(self.args.plot_directory, 'iterations')
plot.bar(plot_file, x, y,
title='Distribution of Iterations of Successful Attacks', xlabel='Number of Iterations', ylabel='Count')
log('[Testing] wrote %s' % plot_file)
reference_perturbations = numpy.zeros(self.perturbations.shape)
if self.args.N_theta > 4:
reference_perturbations[:, 4] = 1
for n in range(len(self.norms)):
norm = self.norms[n]
delta = numpy.linalg.norm(self.perturbations - reference_perturbations, norm, axis=1)
image_delta = numpy.linalg.norm(self.test_images - self.perturbation_images, norm, axis=1)
if self.args.plot_directory and utils.display():
plot_file = os.path.join(self.args.plot_directory, 'distances_l%g' % norm)
plot.histogram(plot_file, delta[raw_overall_success], 50, title='Distribution of $L_{%g}$ Distances of Successful Attacks' % norm,
xlabel='Distance', ylabel='Count')
log('[Testing] wrote %s' % plot_file)
debug_accuracy = numpy.sum(self.accuracy) / self.accuracy.shape[0]
debug_attack_fraction = numpy.sum(raw_overall_success) / numpy.sum(self.success >= 0)
debug_test_fraction = numpy.sum(raw_overall_success) / numpy.sum(self.accuracy)
log('[Testing] attacked mode accuracy: %g' % debug_accuracy)
log('[Testing] only %g of successful attacks are valid' % debug_attack_fraction)
log('[Testing] only %g of correct samples are successfully attacked' % debug_test_fraction)
N_accuracy = numpy.sum(self.accuracy)
self.results[n]['raw_success'] = numpy.sum(raw_overall_success) / N_accuracy
self.results[n]['raw_iteration'] = numpy.average(self.success[raw_overall_success])
self.results[n]['raw_average'] = numpy.average(delta[raw_overall_success]) if numpy.any(raw_overall_success) else 0
self.results[n]['raw_image'] = numpy.average(image_delta[raw_overall_success]) if numpy.any(raw_overall_success) else 0
raw_class_success = numpy.zeros((self.N_class, self.perturbation_codes.shape[0]), bool)
corrected_class_success = numpy.zeros((self.N_class, self.perturbation_codes.shape[0]), bool)
self.results[n]['raw_class_success'] = numpy.zeros((self.N_class))
self.results[n]['raw_class_average'] = numpy.zeros((self.N_class))
self.results[n]['raw_class_image'] = numpy.zeros((self.N_class))
for c in range(self.N_class):
N_samples = numpy.sum(self.accuracy[self.perturbation_codes == c].astype(int))
if N_samples <= 0:
continue;
raw_class_success[c] = numpy.logical_and(raw_overall_success, self.perturbation_codes == c)
self.results[n]['raw_class_success'][c] = numpy.sum(raw_class_success[c]) / N_samples
if numpy.any(raw_class_success[c]):
self.results[n]['raw_class_average'][c] = numpy.average(delta[raw_class_success[c].astype(bool)])
if numpy.any(corrected_class_success[c]):
self.results[n]['raw_class_image'][c] = numpy.average(image_delta[raw_class_success[c].astype(bool)])
if self.args.results_file:
utils.write_pickle(self.args.results_file, self.results)
log('[Testing] wrote %s' % self.args.results_file)
for step_name in step_names:
for instance_name in instance_names:
permanent_deformation, node_labels, _ = read_field_from_odb(
'U',
results_odb_filename,
step_name='gravity',
instance_name=instance_name,
set_name='BALLAST',
get_position_numbers=True,
position=NODAL)
permanent_deformation *= 0
results_odb = odbAccess.openOdb(results_odb_filename,
readOnly=True)
instance = results_odb.rootAssembly.instances[instance_name]
up = np.zeros(len(instance.nodes) * 3)
bc_dofs = []
for bc in boundary_conditions:
set_nodes = []
if bc.type == 'surface':
base = results_odb.rootAssembly.surfaces[bc.set_name]
elif bc.type == 'node_set':
base = results_odb.rootAssembly.nodeSets[bc.set_name]
idx = base.instances.index(instance)
nodes = base.nodes[idx]
print(len(nodes), "in set", bc.set_name)
for n in nodes:
set_nodes.append(3 * (n.label - 1) + bc.component - 1)
bc_dofs.append(3 * (n.label - 1) + bc.component - 1)
bc_dofs = np.unique(np.array(bc_dofs))
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
self.args = None
""" Arguments of program. """
parser = self.get_parser()
if args is not None:
self.args = parser.parse_args(args)
else:
self.args = parser.parse_args()
self.train_images = None
""" (numpy.ndarray) Images to train on. """
self.test_images = None
""" (numpy.ndarray) Images to test on. """
self.train_codes = None
""" (numpy.ndarray) Labels to train on. """
self.test_codes = None
""" (numpy.ndarray) Labels to test on. """
if self.args.log_file:
utils.makedir(os.path.dirname(self.args.log_file))
Log.get_instance().attach(open(self.args.log_file, 'w'))
log('-- ' + self.__class__.__name__)
for key in vars(self.args):
log('[Training] %s=%s' % (key, str(getattr(self.args, key))))
utils.makedir(os.path.dirname(self.args.encoder_file))
utils.makedir(os.path.dirname(self.args.decoder_file))
utils.makedir(os.path.dirname(self.args.log_file))
self.resolution = None
""" (int) Resolution. """
self.encoder = None
""" (models.LearnedVariationalEncoder) Encoder. """
self.decoder = None
""" (models.LearnedDecoder) Decoder. """
self.classifier = None
""" (models.Classifier) Classifier. """
self.encoder_scheduler = None
""" (scheduler.Scheduler) Encoder schduler. """
self.decoder_scheduler = None
""" (scheduler.Scheduler) Decoder schduler. """
self.classifier_scheduler = None
""" (scheduler.Scheduler) Classifier schduler. """
self.random_codes = None
""" (numyp.ndarray) Random codes. """
self.train_statistics = numpy.zeros((0, 15))
""" (numpy.ndarray) Will hold training statistics. """
self.test_statistics = numpy.zeros((0, 12))
""" (numpy.ndarray) Will hold testing statistics. """
self.results = dict()
""" (dict) Results. """
self.logvar = -2.5
""" (float) Log-variance hyper parameter. """
def attack(self):
"""
Test the model.
"""
assert self.model is not None
assert self.model.classifier.training is False
concatenate_axis = -1
if os.path.exists(self.args.perturbations_file) and os.path.exists(self.args.success_file):
self.original_perturbations = utils.read_hdf5(self.args.perturbations_file)
assert len(self.original_perturbations.shape) == 3, self.original_perturbations.shape
log('[Attack] read %s' % self.args.perturbations_file)
self.original_success = utils.read_hdf5(self.args.success_file)
log('[Attack] read %s' % self.args.success_file)
assert self.original_perturbations.shape[0] == self.original_success.shape[0]
assert self.original_perturbations.shape[1] == self.original_success.shape[1]
assert self.original_perturbations.shape[2] == self.test_theta.shape[1]
if self.original_perturbations.shape[1] <= self.args.max_samples and self.original_perturbations.shape[0] <= self.args.max_attempts:
log('[Attack] found %d attempts, %d samples, requested no more' % (self.original_perturbations.shape[0], self.original_perturbations.shape[1]))
return
elif self.original_perturbations.shape[0] == self.args.max_attempts or self.original_perturbations.shape[1] == self.args.max_samples:
if self.original_perturbations.shape[0] == self.args.max_attempts:
self.test_theta = self.test_theta[self.original_perturbations.shape[1]:]
self.test_fonts = self.test_fonts[self.original_perturbations.shape[1]:]
self.test_classes = self.test_classes[self.original_perturbations.shape[1]:]
self.args.max_samples = self.args.max_samples - self.original_perturbations.shape[1]
concatenate_axis = 1
log('[Attack] found %d attempts with %d perturbations, computing %d more perturbations' % (
self.original_perturbations.shape[0], self.original_perturbations.shape[1], self.args.max_samples))
elif self.original_perturbations.shape[1] == self.args.max_samples:
self.args.max_attempts = self.args.max_attempts - self.original_perturbations.shape[0]
concatenate_axis = 0
log('[Attack] found %d attempts with %d perturbations, computing %d more attempts' % (
self.original_perturbations.shape[0], self.original_perturbations.shape[1], self.args.max_attempts))
self.perturbations = numpy.zeros((self.args.max_attempts, self.args.max_samples, self.test_theta.shape[1]))
self.success = numpy.ones((self.args.max_attempts, self.args.max_samples), dtype=int) * -1
if self.args.attack.find('Batch') >= 0:
batch_size = min(self.args.batch_size, self.args.max_samples)
else:
batch_size = 1
objective = self.objective_class()
num_batches = int(math.ceil(self.args.max_samples/batch_size))
for i in range(num_batches):
if i*batch_size == self.args.max_samples:
break
i_start = i * batch_size
i_end = min((i + 1) * batch_size, self.args.max_samples)
batch_fonts = self.test_fonts[i_start: i_end]
batch_classes = self.test_classes[i_start: i_end]
batch_code = numpy.concatenate((common.numpy.one_hot(batch_fonts, self.N_font), common.numpy.one_hot(batch_classes, self.N_class)), axis=1).astype(numpy.float32)
batch_classes = common.torch.as_variable(batch_classes, self.args.use_gpu)
batch_inputs = common.torch.as_variable(self.test_theta[i_start: i_end], self.args.use_gpu)
batch_code = common.torch.as_variable(batch_code, self.args.use_gpu)
t = 0
# This basically allows to only optimize over theta, keeping the font/class code fixed.
self.model.decoder.set_code(batch_code)
while True and t < self.args.max_attempts:
attack = self.setup_attack(batch_inputs, batch_classes)
success, perturbations, probabilities, norm, _ = attack.run(objective)
assert not numpy.any(perturbations != perturbations), perturbations
# Note that we save the perturbed image, not only the perturbation!
perturbations = perturbations.reshape(batch_inputs.size()) # hack for when only one dimensional latent space is used!
self.perturbations[t][i_start: i_end] = perturbations + batch_inputs.cpu().numpy()
self.success[t][i_start: i_end] = success
t += 1
log('[Attack] %d: completed' % i)
if concatenate_axis >= 0:
if self.perturbations.shape[0] == self.args.max_attempts:
self.perturbations = numpy.concatenate((self.original_perturbations, self.perturbations), axis=concatenate_axis)
self.success = numpy.concatenate((self.original_success, self.success), axis=concatenate_axis)
log('[Attack] concatenated')
utils.write_hdf5(self.args.perturbations_file, self.perturbations)
log('[Attack] wrote %s' % self.args.perturbations_file)
utils.write_hdf5(self.args.success_file, self.success)
log('[Attack] wrote %s' % self.args.success_file)
def attack(self):
"""
Test the model.
"""
assert self.model is not None
assert self.model.training is False
assert self.test_images.shape[0] == self.test_codes.shape[0], 'number of samples has to match'
concatenate_axis = -1
if os.path.exists(self.args.perturbations_file) and os.path.exists(self.args.success_file):
self.original_perturbations = utils.read_hdf5(self.args.perturbations_file)
if self.test_images.shape[3] > 1:
assert len(self.original_perturbations.shape) == 5
else:
assert len(self.original_perturbations.shape) == 4
log('[Attack] read %s' % self.args.perturbations_file)
self.original_success = utils.read_hdf5(self.args.success_file)
log('[Attack] read %s' % self.args.success_file)
assert self.original_perturbations.shape[0] == self.original_success.shape[0]
assert self.original_perturbations.shape[1] == self.original_success.shape[1]
assert self.original_perturbations.shape[2] == self.test_images.shape[1]
assert self.original_perturbations.shape[3] == self.test_images.shape[2]#
if self.original_perturbations.shape[1] >= self.args.max_samples and self.original_perturbations.shape[0] >= self.args.max_attempts:
log('[Attack] found %d attempts, %d samples, requested no more' % (self.original_perturbations.shape[0], self.original_perturbations.shape[1]))
return
elif self.original_perturbations.shape[0] == self.args.max_attempts or self.original_perturbations.shape[1] == self.args.max_samples:
if self.original_perturbations.shape[0] == self.args.max_attempts:
self.test_images = self.test_images[self.original_perturbations.shape[1]:]
self.test_codes = self.test_codes[self.original_perturbations.shape[1]:]
self.args.max_samples = self.args.max_samples - self.original_perturbations.shape[1]
concatenate_axis = 1
log('[Attack] found %d attempts with %d perturbations, computing %d more perturbations' % (self.original_perturbations.shape[0], self.original_perturbations.shape[1], self.args.max_samples))
elif self.original_perturbations.shape[1] == self.args.max_samples:
self.args.max_attempts = self.args.max_attempts - self.original_perturbations.shape[0]
concatenate_axis = 0
log('[Attack] found %d attempts with %d perturbations, computing %d more attempts' % (self.original_perturbations.shape[0], self.original_perturbations.shape[1], self.args.max_attempts))
# can't squeeze here!
if self.test_images.shape[3] > 1:
self.perturbations = numpy.zeros((self.args.max_attempts, self.args.max_samples, self.test_images.shape[1], self.test_images.shape[2], self.test_images.shape[3]))
else:
self.perturbations = numpy.zeros((self.args.max_attempts, self.args.max_samples, self.test_images.shape[1], self.test_images.shape[2]))
self.success = numpy.ones((self.args.max_attempts, self.args.max_samples), dtype=int) * -1
if self.args.attack.find('Batch') >= 0:
batch_size = min(self.args.batch_size, self.args.max_samples)
else:
batch_size = 1
objective = self.objective_class()
num_batches = int(math.ceil(self.args.max_samples/batch_size))
for i in range(num_batches): # self.test_images.shape[0]
if i*batch_size == self.args.max_samples:
break
i_start = i*batch_size
i_end = min((i+1)*batch_size, self.args.max_samples)
batch_images = common.torch.as_variable(self.test_images[i_start: i_end], self.args.use_gpu)
batch_classes = common.torch.as_variable(numpy.array(self.test_codes[i_start: i_end]), self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
t = 0
while t < self.args.max_attempts:
attack = self.setup_attack(batch_images, batch_classes)
success, perturbations, probabilities, norm, _ = attack.run(objective)
assert not numpy.any(perturbations != perturbations), perturbations
# Note that we save the perturbed image, not only the perturbation!
self.perturbations[t][i_start: i_end] = numpy.squeeze(numpy.transpose(perturbations + batch_images.cpu().numpy(), (0, 2, 3, 1)))
self.success[t][i_start: i_end] = success
# IMPORTANT: The adversarial examples are not considering whether the classifier is
# actually correct to start with.
t += 1
log('[Attack] %d: completed' % i)
if concatenate_axis >= 0:
if self.perturbations.shape[0] == self.args.max_attempts:
self.perturbations = numpy.concatenate((self.original_perturbations, self.perturbations), axis=concatenate_axis)
self.success = numpy.concatenate((self.original_success, self.success), axis=concatenate_axis)
log('[Attack] concatenated')
utils.write_hdf5(self.args.perturbations_file, self.perturbations)
log('[Attack] wrote %s' % self.args.perturbations_file)
utils.write_hdf5(self.args.success_file, self.success)
log('[Attack] wrote %s' % self.args.success_file)
def attack(self):
"""
Test the model.
"""
assert self.model is not None
assert self.model.training is False
if self.args.attack.find('Batch') >= 0:
batch_size = min(self.args.batch_size, self.args.max_samples)
else:
batch_size = 1
objective = self.objective_class()
num_batches = int(math.ceil(self.args.max_samples / batch_size))
# can't squeeze here!
if self.test_images.shape[3] > 1:
self.perturbations = numpy.zeros(
(self.args.max_attempts, self.args.max_samples,
self.test_images.shape[1], self.test_images.shape[2],
self.test_images.shape[3]))
else:
self.perturbations = numpy.zeros(
(self.args.max_attempts, self.args.max_samples,
self.test_images.shape[1], self.test_images.shape[2]))
self.success = numpy.ones(
(self.args.max_attempts, self.args.max_samples), dtype=int) * -1
self.probabilities = numpy.zeros(
(self.args.max_attempts, self.args.max_samples, self.N_class))
for i in range(num_batches): # self.test_images.shape[0]
if i * batch_size == self.args.max_samples:
break
i_start = i * batch_size
i_end = min((i + 1) * batch_size, self.args.max_samples)
batch_images = numpy.random.randint(0,
255,
size=[batch_size] +
self.test_images.shape[1:])
batch_images = common.torch.as_variable(batch_images,
self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
batch_classes = common.torch.as_variable(
numpy.random.randint(0,
self.N_class - 1,
size=(batch_images.size(0))),
self.args.use_gpu)
t = 0
while t < self.args.max_attempts:
attack = self.setup_attack(batch_images, batch_classes)
success, perturbations, probabilities, norm, _ = attack.run(
objective)
assert not numpy.any(
perturbations != perturbations), perturbations
# Note that we save the perturbed image, not only the perturbation!
self.perturbations[t][i_start:i_end] = numpy.squeeze(
numpy.transpose(perturbations + batch_images.cpu().numpy(),
(0, 2, 3, 1)))
self.success[t][i_start:i_end] = success
self.probabilities[t][i_start:i_end] = probabilities
# IMPORTANT: The adversarial examples are not considering whether the classifier is
# actually correct to start with.
t += 1
log('[Attack] %d: completed' % i)
utils.write_hdf5(self.args.perturbations_file, self.perturbations)
log('[Attack] wrote %s' % self.args.perturbations_file)
utils.write_hdf5(self.args.success_file, self.success)
log('[Attack] wrote %s' % self.args.success_file)
utils.write_hdf5(self.args.probabilities_file, self.probabilities)
log('[Attack] wrote %s' % self.args.probabilities_file)
def compute_nn(self, inclusive=False):
"""
Test detector.
"""
success = numpy.logical_and(self.success >= 0, self.accuracy)
log('[Detection] %d valid attacked samples' % numpy.sum(success))
nearest_neighbor_images = self.nearest_neighbor_images.reshape(self.nearest_neighbor_images.shape[0], -1)
perturbations = self.perturbations.reshape(self.perturbations.shape[0], -1)
test_images = self.test_images.reshape(self.test_images.shape[0], -1)
nearest_neighbors_indices = self.compute_nearest_neighbors(perturbations)
pure_perturbations = perturbations - test_images
log('[Detection] computed nearest neighbors for perturbations')
self.distances['true'] = numpy.zeros((success.shape[0]))
self.distances['test'] = numpy.zeros((success.shape[0]))
self.distances['perturbation'] = numpy.zeros((success.shape[0]))
self.angles['true'] = numpy.zeros((success.shape[0]))
self.angles['test'] = numpy.zeros((success.shape[0]))
self.angles['perturbation'] = numpy.zeros((success.shape[0]))
for n in range(pure_perturbations.shape[0]):
if success[n]:
nearest_neighbors = nearest_neighbor_images[nearest_neighbors_indices[n, :]]
if inclusive:
nearest_neighbors = numpy.concatenate((nearest_neighbors, test_images[n].reshape(1, -1)), axis=0)
nearest_neighbor_mean = test_images[n]
else:
nearest_neighbor_mean = numpy.average(nearest_neighbors, axis=0)
nearest_neighbor_basis = nearest_neighbors - nearest_neighbor_mean
relative_perturbation = perturbations[n] - nearest_neighbor_mean
relative_test_image = test_images[n] - nearest_neighbor_mean
if inclusive:
assert numpy.allclose(relative_test_image, nearest_neighbor_basis[-1])
nearest_neighbor_vectors = numpy.stack((
pure_perturbations[n],
relative_perturbation,
relative_test_image
), axis=1)
nearest_neighbor_projections = common.numpy.project_orthogonal(nearest_neighbor_basis.T, nearest_neighbor_vectors)
assert nearest_neighbor_vectors.shape[0] == nearest_neighbor_projections.shape[0]
assert nearest_neighbor_vectors.shape[1] == nearest_neighbor_projections.shape[1]
angles = numpy.rad2deg(common.numpy.angles(nearest_neighbor_vectors, nearest_neighbor_projections))
distances = numpy.linalg.norm(nearest_neighbor_vectors - nearest_neighbor_projections, ord=2, axis=0)
assert distances.shape[0] == 3
assert angles.shape[0] == 3
self.distances['true'][n] = distances[0]
self.distances['perturbation'][n] = distances[1]
self.distances['test'][n] = distances[2]
self.angles['true'][n] = angles[0]
self.angles['perturbation'][n] = angles[1]
self.angles['test'][n] = angles[2]
log('[Detection] %d: true distance=%g angle=%g' % (n, self.distances['true'][n], self.angles['true'][n]))
log('[Detection] %d: perturbation distance=%g angle=%g' % (n, self.distances['perturbation'][n], self.angles['perturbation'][n]))
log('[Detection] %d: test distance=%g angle=%g' % (n, self.distances['test'][n], self.angles['test'][n]))
self.distances['true'] = self.distances['true'][success]
self.distances['test'] = self.distances['test'][success]
self.distances['perturbation'] = self.distances['perturbation'][success]
self.angles['true'] = self.angles['true'][success]
self.angles['test'] = self.angles['test'][success]
self.angles['perturbation'] = self.angles['perturbation'][success]
if inclusive:
self.distances['test'][:] = 0
self.angles['test'][:] = 0
def compute_true(self):
"""
Compute true.
"""
assert self.test_codes is not None
num_batches = int(math.ceil(self.perturbations.shape[0] / self.args.batch_size))
params = {
'lr': 0.09,
'lr_decay': 0.95,
'lr_min': 0.0000001,
'weight_decay': 0,
}
for b in range(num_batches):
b_start = b * self.args.batch_size
b_end = min((b + 1) * self.args.batch_size, self.perturbations.shape[0])
batch_fonts = self.test_codes[b_start: b_end, 1]
batch_classes = self.test_codes[b_start: b_end, 2]
batch_code = numpy.concatenate((common.numpy.one_hot(batch_fonts, self.N_font), common.numpy.one_hot(batch_classes, self.N_class)), axis=1).astype( numpy.float32)
batch_code = common.torch.as_variable(batch_code, self.args.use_gpu)
batch_images = common.torch.as_variable(self.test_images[b_start: b_end], self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
batch_theta = common.torch.as_variable(self.test_theta[b_start: b_end].astype(numpy.float32), self.args.use_gpu, True)
batch_perturbation = common.torch.as_variable(self.perturbations[b_start: b_end].astype(numpy.float32), self.args.use_gpu)
self.model.set_code(batch_code)
#output_images = self.model.forward(batch_theta)
#test_error = torch.mean(torch.mul(output_images - batch_images, output_images - batch_images))
#print(test_error.item())
#vis.mosaic('true.png', batch_images.cpu().detach().numpy()[:, 0, :, :])
#vis.mosaic('output.png', output_images.cpu().detach().numpy()[:, 0, :, :])
# print(batch_images.cpu().detach().numpy()[0])
# print(output_images.cpu().detach().numpy()[0, 0])
#_batch_images = batch_images.cpu().detach().numpy()
#_output_images = output_images.cpu().detach().numpy()[:, 0, :, :]
#test_error = numpy.max(numpy.abs(_batch_images.reshape(_batch_images.shape[0], -1) - _output_images.reshape(_output_images.shape[0], -1)), axis=1)
#print(test_error)
#test_error = numpy.mean(numpy.multiply(_batch_images - _output_images, _batch_images - _output_images), axis=1)
#print(test_error)
batch_theta = torch.nn.Parameter(batch_theta)
scheduler = ADAMScheduler([batch_theta], **params)
log('[Detection] %d: start' % b)
for t in range(100):
scheduler.update(t//10, float(t)/10)
scheduler.optimizer.zero_grad()
output_perturbation = self.model.forward(batch_theta)
error = torch.mean(torch.mul(output_perturbation - batch_perturbation, output_perturbation - batch_perturbation))
test_error = torch.mean(torch.mul(output_perturbation - batch_images, output_perturbation - batch_images))
#error.backward()
#scheduler.optimizer.step()
log('[Detection] %d: %d = %g, %g' % (b, t, error.item(), test_error.item()))
output_perturbation = numpy.squeeze(numpy.transpose(output_perturbation.cpu().detach().numpy(), (0, 2, 3, 1)))
self.projected_perturbations = common.numpy.concatenate(self.projected_perturbations, output_perturbation)
projected_perturbations = self.projected_perturbations.reshape((self.projected_perturbations.shape[0], -1))
perturbations = self.perturbations.reshape((self.perturbations.shape[0], -1))
success = numpy.logical_and(self.success >= 0, self.accuracy)
log('[Detection] %d valid attacked samples' % numpy.sum(success))
self.distances['true'] = numpy.linalg.norm(perturbations - projected_perturbations, ord=2, axis=1)
self.angles['true'] = numpy.rad2deg(common.numpy.angles(perturbations.T, projected_perturbations.T))
self.distances['true'] = self.distances['true'][success]
self.angles['true'] = self.angles['true'][success]
self.distances['test'] = numpy.zeros((numpy.sum(success)))
self.angles['test'] = numpy.zeros((numpy.sum(success)))
def attack(self):
"""
Test the model.
"""
assert self.model is not None
assert self.model.classifier.training is False
concatenate_axis = -1
if os.path.exists(self.args.perturbations_file) and os.path.exists(
self.args.success_file):
self.original_perturbations = utils.read_hdf5(
self.args.perturbations_file)
assert len(self.original_perturbations.shape) == 3
log('[Attack] read %s' % self.args.perturbations_file)
self.original_success = utils.read_hdf5(self.args.success_file)
log('[Attack] read %s' % self.args.success_file)
assert self.original_perturbations.shape[
0] == self.original_success.shape[0]
assert self.original_perturbations.shape[
1] == self.original_success.shape[1]
if self.original_perturbations.shape[
1] <= self.args.max_samples and self.original_perturbations.shape[
0] <= self.args.max_attempts:
log('[Attack] found %d attempts, %d samples, requested no more'
% (self.original_perturbations.shape[0],
self.original_perturbations.shape[1]))
return
elif self.original_perturbations.shape[
0] == self.args.max_attempts or self.original_perturbations.shape[
1] == self.args.max_samples:
if self.original_perturbations.shape[
0] == self.args.max_attempts:
self.test_images = self.test_images[
self.original_perturbations.shape[1]:]
self.test_codes = self.test_codes[
self.original_perturbations.shape[1]:]
self.args.max_samples = self.args.max_samples - self.original_perturbations.shape[
1]
concatenate_axis = 1
log('[Attack] found %d attempts with %d perturbations, computing %d more perturbations'
% (self.original_perturbations.shape[0],
self.original_perturbations.shape[1],
self.args.max_samples))
elif self.original_perturbations.shape[
1] == self.args.max_samples:
self.args.max_attempts = self.args.max_attempts - self.original_perturbations.shape[
0]
concatenate_axis = 0
log('[Attack] found %d attempts with %d perturbations, computing %d more attempts'
% (self.original_perturbations.shape[0],
self.original_perturbations.shape[1],
self.args.max_attempts))
self.perturbations = numpy.zeros(
(self.args.max_attempts, self.args.max_samples, self.args.N_theta))
self.success = numpy.ones(
(self.args.max_attempts, self.args.max_samples), dtype=int) * -1
if self.args.attack.find('Batch') >= 0:
batch_size = min(self.args.batch_size, self.args.max_samples)
else:
batch_size = 1
objective = self.objective_class()
num_batches = int(math.ceil(self.args.max_samples / batch_size))
for i in range(num_batches):
if i * batch_size == self.args.max_samples:
break
i_start = i * batch_size
i_end = min((i + 1) * batch_size, self.args.max_samples)
batch_classes = common.torch.as_variable(
self.test_codes[i_start:i_end], self.args.use_gpu)
batch_theta = common.torch.as_variable(
numpy.zeros((i_end - i_start, self.args.N_theta),
dtype=numpy.float32), self.args.use_gpu)
if self.args.N_theta > 4:
batch_theta[:, 4] = 1
batch_images = common.torch.as_variable(
self.test_images[i_start:i_end], self.args.use_gpu)
batch_images = batch_images.permute(0, 3, 1, 2)
self.model.decoder.set_image(batch_images)
#output_images = self.model.decoder.forward(batch_theta)
#error = torch.sum(torch.abs(output_images - batch_images))
#error = error.item()
#print(error)
#from matplotlib import pyplot
#output_images = numpy.squeeze(numpy.transpose(output_images.cpu().detach().numpy(), (0, 2, 3, 1)))
#pyplot.imshow(output_images[0])
#pyplot.show()
t = 0
while True and t < self.args.max_attempts:
attack = self.setup_attack(batch_theta, batch_classes)
success, perturbations, probabilities, norm, _ = attack.run(
objective)
assert not numpy.any(
perturbations != perturbations), perturbations
# Note that we save the perturbed image, not only the perturbation!
perturbations = perturbations.reshape(batch_theta.size(
)) # hack for when only one dimensional latent space is used!
self.perturbations[t][
i_start:i_end] = perturbations + batch_theta.cpu().detach(
).numpy()
self.success[t][i_start:i_end] = success
t += 1
log('[Attack] %d: completed' % i)
if concatenate_axis >= 0:
if self.perturbations.shape[0] == self.args.max_attempts:
self.perturbations = numpy.concatenate(
(self.original_perturbations, self.perturbations),
axis=concatenate_axis)
self.success = numpy.concatenate(
(self.original_success, self.success),
axis=concatenate_axis)
log('[Attack] concatenated')
utils.write_hdf5(self.args.perturbations_file, self.perturbations)
log('[Attack] wrote %s' % self.args.perturbations_file)
utils.write_hdf5(self.args.success_file, self.success)
log('[Attack] wrote %s' % self.args.success_file)
