def compute_ppca(self):
"""
Compute PPCA.
"""
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
ppca = PPCA(n_components=self.args.n_pca)
ppca.fit(nearest_neighbor_images)
log('[Experiment] computed PPCA on nearest neighbor images')
reconstructed_test_images = ppca.inverse_transform(ppca.transform(test_images))
reconstructed_perturbations = ppca.inverse_transform(ppca.transform(perturbations))
reconstructed_pure_perturbations = ppca.inverse_transform(ppca.transform(pure_perturbations))
self.distances['test'] = numpy.average(numpy.multiply(reconstructed_test_images - test_images, reconstructed_test_images - test_images), axis=1)
self.distances['perturbation'] = numpy.average(numpy.multiply(reconstructed_perturbations - perturbations, reconstructed_perturbations - perturbations), axis=1)
self.distances['true'] = numpy.average(numpy.multiply(reconstructed_pure_perturbations - pure_perturbations, reconstructed_pure_perturbations - pure_perturbations), axis=1)
self.angles['test'] = numpy.rad2deg(common.numpy.angles(test_images.T, reconstructed_test_images.T))
self.angles['perturbation'] = numpy.rad2deg(common.numpy.angles(reconstructed_perturbations.T, perturbations.T))
self.angles['true'] = numpy.rad2deg(common.numpy.angles(reconstructed_pure_perturbations.T, pure_perturbations.T))
self.distances['test'] = self.distances['test'][success]
self.distances['perturbation'] = self.distances['perturbation'][success]
self.distances['true'] = self.distances['true'][success]
def compute_normalized_ppca(self):
"""
Compute PPCA.
"""
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_neighbor_images_norms = numpy.linalg.norm(nearest_neighbor_images, ord=2, axis=1)
perturbations_norms = numpy.linalg.norm(perturbations, ord=2, axis=1)
test_images_norms = numpy.linalg.norm(test_images, ord=2, axis=1)
pure_perturbations_norms = numpy.linalg.norm(pure_perturbations, ord=2, axis=1)
success = numpy.logical_and(numpy.logical_and(self.success >= 0, self.accuracy), pure_perturbations_norms > 1e-4)
log('[Detection] %d valid attacked samples' % numpy.sum(success))
perturbations_norms = perturbations_norms[success]
test_images_norms = test_images_norms[success]
pure_perturbations_norms = pure_perturbations_norms[success]
perturbations = perturbations[success]
test_images = test_images[success]
pure_perturbations = pure_perturbations[success]
nearest_neighbor_images /= numpy.repeat(nearest_neighbor_images_norms.reshape(-1, 1), nearest_neighbor_images.shape[1], axis=1)
perturbations /= numpy.repeat(perturbations_norms.reshape(-1, 1), perturbations.shape[1], axis=1)
test_images /= numpy.repeat(test_images_norms.reshape(-1, 1), test_images.shape[1], axis=1)
pure_perturbations /= numpy.repeat(pure_perturbations_norms.reshape(-1, 1), pure_perturbations.shape[1], axis=1)
assert not numpy.any(nearest_neighbor_images != nearest_neighbor_images)
assert not numpy.any(perturbations != perturbations)
assert not numpy.any(test_images != test_images)
assert not numpy.any(pure_perturbations != pure_perturbations)
ppca = PPCA(n_components=self.args.n_pca)
ppca.fit(nearest_neighbor_images)
log('[Experiment] computed PPCA on nearest neighbor images')
reconstructed_test_images = ppca.inverse_transform(ppca.transform(test_images))
reconstructed_perturbations = ppca.inverse_transform(ppca.transform(perturbations))
reconstructed_pure_perturbations = ppca.inverse_transform(ppca.transform(pure_perturbations))
#self.probabilities['test'] = ppca.marginal(test_images)
#self.probabilities['perturbation'] = ppca.marginal(perturbations)
#self.probabilities['true'] = ppca.marginal(pure_perturbations)
self.distances['test'] = numpy.average(numpy.multiply(reconstructed_test_images - test_images, reconstructed_test_images - test_images), axis=1)
self.distances['perturbation'] = numpy.average(numpy.multiply(reconstructed_perturbations - perturbations, reconstructed_perturbations - perturbations), axis=1)
self.distances['true'] = numpy.average(numpy.multiply(reconstructed_pure_perturbations - pure_perturbations, reconstructed_pure_perturbations - pure_perturbations), axis=1)
self.angles['test'] = numpy.rad2deg(common.numpy.angles(test_images.T, reconstructed_test_images.T))
self.angles['perturbation'] = numpy.rad2deg(common.numpy.angles(reconstructed_perturbations.T, perturbations.T))
self.angles['true'] = numpy.rad2deg(common.numpy.angles(reconstructed_pure_perturbations.T, pure_perturbations.T))
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.
"""
# 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)