def adversarialAccuracy(model):
keras_model = KerasModel(model, bounds=(0, 1), channel_axis=channel_axis)
criterion = Misclassification()
length = x_test.shape[0]
wrong = 0
period = 50
for i in range(length):
image, label = x_test[i], y_test_original[i]
#attack = foolbox.attacks.FGSM(keras_model, criterion)
#image_adv = attack(image, label, epsilons=5, max_epsilon=1.0)
pgd2 = foolbox.attacks.L2BasicIterativeAttack(keras_model, criterion)
image_adv = pgd2(image,
label,
epsilon=1.0,
stepsize=1.0,
iterations=1,
binary_search=False)
if image_adv is not None:
prediction = np.argmax(
keras_model.predictions_and_gradient(image_adv, label)[0])
assert prediction != label
wrong += 1
if i % period == period - 1:
print("Adversarial attack success rate: {} / {} = {}".format(
wrong, i + 1, wrong / (i + 1)))
if image_adv is not None:
displayImage(image_adv, label)
print("Size of perturbation: {}".format(
LA.norm(image_adv - image, None)))
print("Adversarial error rate: {} / {} = {}".format(
wrong, length, wrong / length))
def test_keras_model_preprocess():
num_classes = 1000
bounds = (0, 255)
channels = num_classes
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
inputs = Input(shape=(5, 5, channels))
logits = GlobalAveragePooling2D(
data_format='channels_last')(inputs)
preprocessing = (np.arange(num_classes)[None, None],
np.random.uniform(size=(5, 5, channels)) + 1)
model1 = KerasModel(
Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits')
model2 = KerasModel(
Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits',
preprocessing=preprocessing)
model3 = KerasModel(
Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits')
preprocessing = (0, np.random.uniform(size=(5, 5, channels)) + 1)
model4 = KerasModel(
Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits',
preprocessing=preprocessing)
np.random.seed(22)
test_images = np.random.rand(2, 5, 5, channels).astype(np.float32)
test_images_copy = test_images.copy()
p1 = model1.batch_predictions(test_images)
p2 = model2.batch_predictions(test_images)
# make sure the images have not been changed by
# the in-place preprocessing
assert np.all(test_images == test_images_copy)
p3 = model3.batch_predictions(test_images)
assert p1.shape == p2.shape == p3.shape == (2, num_classes)
np.testing.assert_array_almost_equal(
p1 - p1.max(),
p3 - p3.max(),
decimal=5)
model4.batch_predictions(test_images)
def sample_attack(keras_model,
image,
attack_method,
input_name,
labeled,
target=0):
"""
对于给定的模型和输入，本方法将使用指定的攻击方法生成攻击图片
:return:
"""
input_shape = nm.extract_input_shape(keras_model)[1:]
image = nm.prepare_image(image, input_shape)
layer_input = [image]
label = keras_model.predict(np.asarray(layer_input))
label = np.argmax(label)
if labeled is not None and not label == labeled:
return None, "This image cannot be correctly classified, no adversarial sample will be generated. expected: " + str(
labeled) + " actual: " + str(label)
network_model = KerasModel(keras_model, bounds=(0, 1))
# run the attack
if str(attack_method).lower() == 'lbfgs':
attack = LBFGSAttack(model=network_model,
criterion=TargetClassProbability(target, p=.5))
elif str(attack_method).lower() == 'singlepixelattack':
attack = SinglePixelAttack(model=network_model,
criterion=TargetClassProbability(target,
p=.5))
else:
return "Attack method not supported at the moment"
print(label)
if label == target:
target = (target + 1) % 10
adversarial = attack(image[:, :, ::-1], label)
output = network_model.predictions(adversarial)
print(np.argmax(output))
adversarial = adversarial.reshape(input_shape)
adversarial = adversarial * 255
adv_image_name = 'adv_{}_origin_{}_{}_{}'.format(target, label,
attack_method, input_name)
print(adversarial.shape)
im = None
if len(adversarial.shape) == 2:
im = Image.fromarray(np.uint8(adversarial), mode="1")
if len(adversarial.shape) == 3 and adversarial.shape[2] == 1:
im = Image.fromarray(np.uint8(
adversarial.reshape(adversarial.shape[0], adversarial.shape[1])),
mode="L")
if len(adversarial.shape) == 3 and adversarial.shape[2] == 3:
im = Image.fromarray(np.uint8(adversarial), mode="RGB")
im.save(os.path.join(basedir, Config.UPLOAD_IMAGE_FOLDER, adv_image_name))
# cv2.imwrite(os.path.join(basedir, Config.UPLOAD_IMAGE_FOLDER, adv_image_name), adversarial)
print('adv', adv_image_name)
return adversarial, adv_image_name
def test_keras_backward(num_classes):
bounds = (0, 255)
channels = num_classes
model = Sequential()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
model.add(GlobalAveragePooling2D(
data_format='channels_last', input_shape=(5, 5, channels)))
model = KerasModel(
model,
bounds=bounds,
predicts='logits')
test_image = np.random.rand(5, 5, channels).astype(np.float32)
test_grad_pre = np.random.rand(num_classes).astype(np.float32)
test_grad = model.backward(test_grad_pre, test_image)
assert test_grad.shape == test_image.shape
manual_grad = np.repeat(np.repeat(
(test_grad_pre / 25.).reshape((1, 1, -1)),
5, axis=0), 5, axis=1)
np.testing.assert_almost_equal(
test_grad,
manual_grad)
def test_keras_model(num_classes):
bounds = (0, 255)
channels = num_classes
model = Sequential()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
model.add(
GlobalAveragePooling2D(data_format="channels_last",
input_shape=(5, 5, channels)))
model = KerasModel(model, bounds=bounds, predicts="logits")
test_images = np.random.rand(2, 5, 5, channels).astype(np.float32)
test_label = 7
assert model.forward(test_images).shape == (2, num_classes)
test_logits = model.forward_one(test_images[0])
assert test_logits.shape == (num_classes, )
test_gradient = model.gradient_one(test_images[0], test_label)
assert test_gradient.shape == test_images[0].shape
np.testing.assert_almost_equal(
model.forward_and_gradient_one(test_images[0], test_label)[0],
test_logits)
np.testing.assert_almost_equal(
model.forward_and_gradient_one(test_images[0], test_label)[1],
test_gradient)
assert model.num_classes() == num_classes
def test_keras_model(num_classes):
bounds = (0, 255)
channels = num_classes
model = Sequential()
model.add(
GlobalAveragePooling2D(data_format='channels_last',
input_shape=(5, 5, channels)))
model = KerasModel(model, bounds=bounds, predicts='logits')
test_images = np.random.rand(2, 5, 5, channels).astype(np.float32)
test_label = 7
assert model.batch_predictions(test_images).shape \
== (2, num_classes)
test_logits = model.predictions(test_images[0])
assert test_logits.shape == (num_classes, )
test_gradient = model.gradient(test_images[0], test_label)
assert test_gradient.shape == test_images[0].shape
np.testing.assert_almost_equal(
model.predictions_and_gradient(test_images[0], test_label)[0],
test_logits)
np.testing.assert_almost_equal(
model.predictions_and_gradient(test_images[0], test_label)[1],
test_gradient)
assert model.num_classes() == num_classes
def __init__(self,
model,
bounds,
channel_axis=3,
preprocessing=(0, 1),
predicts='probabilities',
entropy_mask=True,
cache_grad_mask=False):
super(FoolboxKerasModelEntropy,
self).__init__(bounds=bounds,
channel_axis=channel_axis,
preprocessing=preprocessing)
self.entropy_mask = entropy_mask
self.grad_mask = None
self.cache_grad_mask = cache_grad_mask
self.keras_model = KerasModel(model, bounds, channel_axis,
preprocessing, predicts)
def setup_local_model():
#sets up local ResNet50 model, to use for local testing
keras.backend.set_learning_phase(0)
kmodel = keras.applications.resnet50.ResNet50(weights='imagenet')
preprocessing = (np.array([104, 116, 123]), 1)
model = KerasModel(kmodel,
bounds=(0, 255),
preprocessing=preprocessing,
predicts='logits')
return model
def test_keras_model_forward_gradients():
num_classes = 1000
bounds = (0, 255)
channels = num_classes
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
inputs = Input(shape=(5, 5, channels))
logits = GlobalAveragePooling2D(data_format="channels_last")(inputs)
preprocessing = (
np.arange(num_classes)[None, None],
np.random.uniform(size=(5, 5, channels)) + 1,
)
model = KerasModel(
Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts="logits",
preprocessing=preprocessing,
)
eps = 1e-3
np.random.seed(22)
test_images = np.random.rand(5, 5, 5, channels).astype(np.float32)
test_labels = [7] * 5
_, g1 = model.forward_and_gradient(test_images, test_labels)
test_label_array = np.array([test_labels])
l1 = model._loss_fn([test_images - eps / 2 * g1, test_label_array])[0]
l2 = model._loss_fn([test_images + eps / 2 * g1, test_label_array])[0]
assert 1e5 * (l2 - l1) > 1
# make sure that gradient is numerically correct
np.testing.assert_array_almost_equal(1e5 * (l2 - l1),
1e5 * eps * np.linalg.norm(g1)**2,
decimal=1)
def test_keras_model_gradients():
num_classes = 1000
bounds = (0, 255)
channels = num_classes
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
inputs = Input(shape=(5, 5, channels))
logits = GlobalAveragePooling2D(
data_format='channels_last')(inputs)
preprocessing = (np.arange(num_classes)[None, None],
np.random.uniform(size=(5, 5, channels)) + 1)
model = KerasModel(
Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits',
preprocessing=preprocessing)
eps = 1e-3
np.random.seed(22)
test_image = np.random.rand(5, 5, channels).astype(np.float32)
test_label = 7
_, g1 = model.predictions_and_gradient(test_image, test_label)
test_label_array = np.array([test_label])
l1 = model._loss_fn([test_image[None] - eps / 2 * g1, test_label_array])[0]
l2 = model._loss_fn([test_image[None] + eps / 2 * g1, test_label_array])[0]
assert 1e5 * (l2 - l1) > 1
# make sure that gradient is numerically correct
np.testing.assert_array_almost_equal(
1e5 * (l2 - l1),
1e5 * eps * np.linalg.norm(g1)**2,
decimal=1)
def test_keras_model_probs(num_classes):
bounds = (0, 255)
channels = num_classes
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
inputs = Input(shape=(5, 5, channels))
logits = GlobalAveragePooling2D(
data_format='channels_last')(inputs)
probs = Activation(softmax)(logits)
model1 = KerasModel(
Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits')
model2 = KerasModel(
Model(inputs=inputs, outputs=probs),
bounds=bounds,
predicts='probabilities')
model3 = KerasModel(
Model(inputs=inputs, outputs=probs),
bounds=bounds,
predicts='probs')
np.random.seed(22)
test_images = np.random.rand(2, 5, 5, channels).astype(np.float32)
p1 = model1.batch_predictions(test_images)
p2 = model2.batch_predictions(test_images)
p3 = model3.batch_predictions(test_images)
assert p1.shape == p2.shape == p3.shape == (2, num_classes)
np.testing.assert_array_almost_equal(
p1 - p1.max(),
p2 - p2.max(),
decimal=1)
np.testing.assert_array_almost_equal(
p2 - p2.max(),
p3 - p3.max(),
decimal=5)
def test_keras_model_preprocess():
num_classes = 1000
bounds = (0, 255)
channels = num_classes
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
inputs = Input(shape=(5, 5, channels))
logits = GlobalAveragePooling2D(data_format='channels_last')(inputs)
preprocessing = (np.arange(num_classes)[None, None],
np.random.uniform(size=(5, 5, channels)) + 1)
model1 = KerasModel(Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits')
model2 = KerasModel(Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits',
preprocessing=preprocessing)
model3 = KerasModel(Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits')
np.random.seed(22)
test_images = np.random.rand(2, 5, 5, channels).astype(np.float32)
test_images_copy = test_images.copy()
p1 = model1.batch_predictions(test_images)
p2 = model2.batch_predictions(test_images)
# make sure the images have not been changed by
# the in-place preprocessing
assert np.all(test_images == test_images_copy)
p3 = model3.batch_predictions(test_images)
assert p1.shape == p2.shape == p3.shape == (2, num_classes)
np.testing.assert_array_almost_equal(p1 - p1.max(),
p3 - p3.max(),
decimal=5)
def test_keras_model_preprocess():
num_classes = 1000
bounds = (0, 255)
channels = num_classes
inputs = Input(shape=(5, 5, channels))
logits = GlobalAveragePooling2D(data_format='channels_last')(inputs)
def preprocess_fn(x):
# modify x in-place
x /= 2
return x
model1 = KerasModel(Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits')
model2 = KerasModel(Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits',
preprocess_fn=preprocess_fn)
model3 = KerasModel(Model(inputs=inputs, outputs=logits),
bounds=bounds,
predicts='logits')
np.random.seed(22)
test_images = np.random.rand(2, 5, 5, channels).astype(np.float32)
test_images_copy = test_images.copy()
p1 = model1.batch_predictions(test_images)
p2 = model2.batch_predictions(test_images)
# make sure the images have not been changed by
# the in-place preprocessing
assert np.all(test_images == test_images_copy)
p3 = model3.batch_predictions(test_images)
assert p1.shape == p2.shape == p3.shape == (2, num_classes)
np.testing.assert_array_almost_equal(p1 - p1.max(),
p3 - p3.max(),
decimal=5)
def main():
# load keras model
kmodel = load_model('./models/cnn_model_10bar_ohlc.h5')
# load data
data = pro.load_pkl('./data/label8_eurusd_10bar_1500_500_val200_gaf.pkl')
train_x = data['train_ohlc_gaf']
train_label = data['train_label_onehot']
# create foolbox model
fmodel = KerasModel(kmodel, bounds=(-1, 1))
# create our modified attack model
MODIFIED_LocalSearchAttack = foolbox.attacks.LocalSearchAttack(model=fmodel)
# generate fake data
generate_adversarial_examples(kmodel = kmodel, fmodel = fmodel, attacker = MODIFIED_LocalSearchAttack,
x_data = train_x, y_label = train_label)
def main(params): # load data data = pro.load_pkl(params['pkl_name']) # load our model kmodel = load_model(params['model_name']) # create foolbox model fmodel = KerasModel(kmodel, bounds=(-1, 1)) # customized LocalSearchAttack attack = foolbox.attacks.LocalSearchAttack(model=fmodel) # attack all samples results = attack_all_samples(data, kmodel, fmodel, attack)
def main():
# model wont work with eager execution enable
tf.compat.v1.disable_eager_execution()
batch_size = 128
num_classes = 10
epochs = 12
model = mnistmodel.MnistModel(batch_size, num_classes, epochs)
white_box = model.build_model()
x_sample, y_sample = model.get_samples()
#image_plot = plt.imshow(np.reshape(x_sample*255, (28, 28)))
#plt.show()
# no attack
label = np.argmax(white_box.predict(np.reshape(x_sample, (1, 28, 28, 1))),
axis=-1)[0]
label = np.asarray(label)
print(label)
# attack
preprocessing = dict(flip_axis=-1)
fmodel = KerasModel(white_box,
bounds=(0, 255),
preprocessing=preprocessing)
attack = attacks.FGSM(fmodel)
adversarial = attack(x_sample, y_sample)
image_plot = plt.imshow(np.reshape(adversarial, (28, 28)))
plt.show()
label = np.argmax(white_box.predict(np.reshape(adversarial,
(1, 28, 28, 1))),
axis=-1)[0]
print(label)
def perturb_lbfgs(sample, model, data):
# Perturb images using LBFGS attack by Szegedy et al. using the foolbox library
# Based on the tutorial: https://foolbox.readthedocs.io/en/latest/user/tutorial.html
# create model for foolbox
foolbox_model = KerasModel(model, (0.0, 1.0), channel_axis=1)
#foolbox_model = TheanoModel(model.input, model.layers[-2].output, (0.0, 1.0), 10, channel_axis=1)
# get correct class
correct_class = model.predict_classes(sample)
# set target to be next higher class (and 0 for 9)
target_class = (correct_class+1)%10
# set attack criterion to be 90% target class probability
criterion = TargetClassProbability(target_class, p=0.90)
# create attack on model with given criterion
attack = LBFGSAttack()
#print(sample[0,:,:,:].shape)
# generate adversarial example
# sample needs to be transformed from (batchsize, channels, rows, cols) format to (height, width, channels) for
# foolbox, but that leads to problems with the model
transformed_sample = sample.reshape(28,28,1)
ad_ins = Adversarial(foolbox_model, criterion, transformed_sample, correct_class)
adversarial = attack(ad_ins)
# get class of adversarial example
pred_class = model.predict_classes(adversarial)
if pred_class != correct_class:
return (1, adversarial)
return (0, sample)
from foolbox.criteria import OriginalClassProbability, Misclassification, ConfidentMisclassification
from skimage.measure import compare_ssim as ssim
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images,
test_labels) = fashion_mnist.load_data()
train_images = np.expand_dims(train_images, axis=3)
test_images = np.expand_dims(test_images, axis=3)
train_images = train_images / 255.0
test_images = test_images / 255.0
trained_model = keras.models.load_model('fashionMNIST.h5')
cnn5 = keras.models.load_model('cnn5.h5')
sub_model = KerasModel(trained_model, bounds=(0, 1), channel_axis=1)
testNum = 100
randArray = np.random.choice(10000, testNum, replace=False)
def diff_ssim(img1, img2):
img1 = (img1 * 255).reshape([28, 28])
img2 = (img2 * 255).reshape([28, 28])
return ssim(img1, img2)
attackRate_u2 = 0.0
attackSSIM_u2 = 0.0
count = 0
class FoolboxKerasModelEntropy(DifferentiableModel):
def __init__(self,
model,
bounds,
channel_axis=3,
preprocessing=(0, 1),
predicts='probabilities',
entropy_mask=True,
cache_grad_mask=False):
super(FoolboxKerasModelEntropy,
self).__init__(bounds=bounds,
channel_axis=channel_axis,
preprocessing=preprocessing)
self.entropy_mask = entropy_mask
self.grad_mask = None
self.cache_grad_mask = cache_grad_mask
self.keras_model = KerasModel(model, bounds, channel_axis,
preprocessing, predicts)
def compute_gradient_mask(self, image):
gray = skimage.color.rgb2gray(image)
mask = skimage.filters.rank.entropy(gray, skimage.morphology.disk(3))
low = mask < 4.2
high = mask >= 4.2
mask[low] = 0.0
mask[high] = 1.0
self.grad_mask = np.broadcast_to(
mask.reshape(mask.shape[0] * mask.shape[1], 1),
(mask.shape[0] * mask.shape[1], image.shape[2])).reshape(
image.shape)
def __mask_gradient(self, grad, image):
if self.entropy_mask is True:
if self.cache_grad_mask is True:
return grad * self.grad_mask
else:
mask = utils.image2mask(image)
mask = np.broadcast_to(
mask.reshape(mask.shape[0] * mask.shape[1], 1),
(mask.shape[0] * mask.shape[1], image.shape[2])).reshape(
image.shape)
return grad * mask
else:
return grad
def predictions_and_gradient(self, image, label):
"""Calculates predictions for an image and the gradient of
the cross-entropy loss w.r.t. the image.
Parameters
----------
image : `numpy.ndarray`
Single input with shape as expected by the model
(without the batch dimension).
label : int
Reference label used to calculate the gradient.
Returns
-------
predictions : `numpy.ndarray`
Vector of predictions (logits, i.e. before the softmax) with
shape (number of classes,).
gradient : `numpy.ndarray`
The gradient of the cross-entropy loss w.r.t. the image. Will
have the same shape as the image.
See Also
--------
:meth:`gradient`
"""
pred, grad = self.keras_model.predictions_and_gradient(image, label)
return pred, self.__mask_gradient(grad, image)
def num_classes(self):
return self.keras_model.num_classes()
def batch_predictions(self, images):
return self.keras_model.batch_predictions(images)
def backward(self, gradient, image):
"""Backpropagates the gradient of some loss w.r.t. the logits
through the network and returns the gradient of that loss w.r.t
to the input image.
Parameters
----------
gradient : `numpy.ndarray`
Gradient of some loss w.r.t. the logits.
image : `numpy.ndarray`
Single input with shape as expected by the model
(without the batch dimension).
Returns
-------
gradient : `numpy.ndarray`
The gradient w.r.t the image.
See Also
--------
:meth:`gradient`
"""
grad = self.keras_model.backward(gradient, image)
return self.__mask_gradient(grad, image)
model = deep.five_layer_dnn_model_wide(input_shape, output_shape,
0, l1, 0)
elif args.experiment_type == "six_layer":
model = deep.six_layer_dnn_model_wide(input_shape, output_shape, 0,
l1, 0)
elif args.experiment_type == "VGG":
model = convolutional.vgg_model_wide(args.dataset, 0, l1, 0)
elif args.experiment_type == "leNet":
model = convolutional.leNet_model_wide(0, l1, 0)
else:
raise Exception("Invalid model!")
model.fit(x_train, y_train, epochs=50, batch_size=128)
preds = np.argmax(model.predict(x_test), axis=1)
kmodel = KerasModel(model=model, bounds=(min_, max_))
attack = None
if args.attack_type == 'l2':
attack = CarliniWagnerL2Attack(kmodel, TargetClass(7))
elif args.attack_type == 'linf':
attack = RandomPGD(kmodel, TargetClass(7))
x_sample = np.take(x_test, ones, axis=0)
# We exclude by default those examples which are not predicted by the classifier as 1s.
true_ones = np.where(preds == 1)[0]
x_sample = np.take(x_sample, true_ones, axis=0)
y_sample = np.array([to_one_hot(1) for _ in x_sample])
# Load two images. The cat image is original image
# and the dog image is used to initialize a targeted
# attack.
dog_img = image.load_img('dog.jpg', target_size=(224, 224))
cat_img = image.load_img('cat.jpg', target_size=(224, 224))
dog_img = image.img_to_array(dog_img)
cat_img = image.img_to_array(cat_img)
cat_img = 2.0 * cat_img / 255.0 - 1
dog_img = 2.0 * dog_img / 255.0 - 1
dog_x = np.expand_dims(dog_img, axis=0)
cat_x = np.expand_dims(cat_img, axis=0)
# Build a foolbox model
fmodel = KerasModel(kmodel, bounds=(-1, 1))
# label of the target class
preds = kmodel.predict(dog_x)
dog_label = np.argmax(preds)
# label of the original class
preds = kmodel.predict(cat_x)
cat_label = np.argmax(preds)
criterion_1 = TopKMisclassification(k=5)
criterion_2 = TargetClass(dog_label)
criterion_3 = TargetClassProbability(dog_label, p=0.5)
criterion = criterion_1 & criterion_2 & criterion_3
attack = BoundaryAttack(model=fmodel, criterion=criterion)
print("adversarial not found")
return
adverse = np.round(adversarial_image)
original_category = categories[original_class]
target_category = categories[target_class]
original_image_name = file_name[:-4]
save_path = "adversarial_examples/" + original_category + "/"
saved_image_name = original_image_name + "_" + target_category + ".png"
matplotlib.image.imsave(save_path+saved_image_name, adverse/255, format = 'png')
#----------------creating the adversarial Model------------------------#
keras.backend.set_learning_phase(0)
network = load_model("classifier(final).h5")
adversarial_model = KerasModel(network,
bounds = (0, 255),
preprocessing = (0, 1))
attack = ProjectedGradientDescentAttack()
#----------------------------Creating the directories------------------#
base_directory = "all_years_140x140"
categories = os.listdir(base_directory)
while len(categories) != 13:
del categories[0]
for category in categories:
adversary_path = "adversarial_examples/" + category
if not os.path.exists(adversary_path):
os.makedirs(adversary_path)
if os.path.isdir(base_directory + "/" + category):
images = os.listdir(base_directory + "/" + category)
import foolbox
from foolbox.models import KerasModel
from foolbox.attacks import LBFGSAttack
from foolbox.criteria import TargetClassProbability, Misclassification
import numpy as np
import keras
from keras.models import load_model
import matplotlib.pyplot as plt
kmodel = load_model('./LeNet.h5')
preprocessing = (np.array([104, 116, 123]), 1)
fmodel = KerasModel(kmodel, bounds=(0, 255))
attack = LBFGSAttack(model=fmodel, criterion=Misclassification())
adversarial_imgs = []
adversarial_labels =[]
# adversarial_imgs = np.asarray(adversarial_imgs)
# adversarial_labels = np.asarray(adversarial_labels)
# print(type(adversarial_imgs))
img_temp = np.load('./mnist_pure/x_train.npy')
# print(img_temp.shape)
img_temp = np.asarray(img_temp, dtype=np.float32)
# print(img_temp[0].shape)
label_temp = np.load('./mnist_pure/y_train.npy')
label_temp= np.asarray(label_temp, dtype=np.float32)
for i in range(0,60000):
adversarial = attack(img_temp[i], label_temp[i])
adversarial_imgs.append(adversarial)
def main():
# Load Keras model
model = load_model(
r'.................................................h5')
Ptype = 'probabilities' # (default) with the softmax
# # Switch softmax with linear activations -- to avoid the softmax
#model = force_linear_activation(model=model, savemodel=None)
#Ptype = 'logits'
compressJPEG = 0 #'true'
jpeg = 0
jpeg_quality = 85
# size (no color images)
img_rows, img_cols, img_chans = 64, 64, 1
num_classes = 2
#---------------------------------------------------------
# Load test data, define labels, test the model
#-----------------------------------------------------------
images = glob(r'F:..................................\*.png')
label = 1 # label = 0 for Manipulated, 1 for Original ------ for StammNets, it is the reverse ! (0 for Original)
#number of imagess for testing the model
#numImg = len(images) # <= len(images)
numImg = 100
#np.random.seed(1234)
#index = np.random.randint(len(images), size=numImg)
index = np.arange(numImg)
x_test = np.zeros((numImg, img_rows, img_cols))
for i in np.arange(numImg):
img = imread(images[
index[i]]) # Flatten=True means convert to gray on the fly
if compressJPEG:
img1 = Image.fromarray(img)
img1.save('temp.jpeg', "JPEG", quality=jpeg_quality)
img = Image.open('temp.jpeg')
x_test[i] = img
# Labels
y_test_c = np.tile(label, numImg)
# Convert labels to one-hot with Keras
y_test = keras.utils.to_categorical(y_test_c, num_classes)
# Reshape test data, divide by 255 because net was trained this way
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, img_chans)
x_test = x_test.astype('float32')
x_test /= 255
# Test legitimate examples
score = model.evaluate(x_test, y_test, verbose=0)
predicted_legitimate_labels = np.argmax(model.predict(x_test), axis=1)
print('Accuracy on legitimate images (all): {:3.4f}'.format(score[1]))
# ----------------------------------------------------------------------------------------------------------------------
# Attack the [correctly classified] images in the test set
# ----------------------------------------------------------------------------------------------------------------------
# Wrap model
fmodel = KerasModel(model, bounds=(0, 1), predicts=Ptype)
#KK: KerasModel Creates a Model instance from a Keras model.
# Prepare attack
#attack = foolbox.attacks.IterativeGradientSignAttack(fmodel)
#######attack = foolbox.attacks.DeepFoolAttack(fmodel)
attack = foolbox.attacks.SaliencyMapAttack(fmodel)
#attack = foolbox.attacks.BIM(fmodel)
#attack = foolbox.attacks.LBFGSAttack(fmodel)
# ------Get data, labels and categorical labels ***only for correctly classified examples***
l = np.argwhere(predicted_legitimate_labels == y_test_c).shape[0]
#this is the number of legitimate images correctly classified
x_test_ok = np.reshape(
x_test[
np.array(np.argwhere(
predicted_legitimate_labels == y_test_c)), :, :, :],
(l, img_rows, img_cols, img_chans))
#put the correctly classified images in a Numpy array x_test_ok
y_test_ok = np.reshape(
y_test[np.argwhere(predicted_legitimate_labels == y_test_c), :],
(l, num_classes))
y_test_c_ok = np.argmax(y_test_ok, axis=1)
# ------------------
# Elaborate n_test adversarial examples ***only for correctly classified examples*** (at most l)
n_test = l #150 # it must be lower than l
#how many many images out of the correctly classified you want to try to attack!
S = 0
S_jpg = 0
avg_Max_dist = 0
avg_L1_dist = 0
avg_No_Mod_Pixels = 0
t = 0
avg_psnr = 0
PSNR = 0
psnr_org = 0
adv_images = np.zeros((n_test, img_rows, img_cols, img_chans))
true_labels_cat = []
for idx in np.arange(n_test):
image = x_test_ok[idx]
true_labels_cat.append(y_test_ok[idx, :])
image = image.astype('float32')
if compressJPEG:
img1 = Image.fromarray(np.uint8(255 * image[:, :, 0]))
img1.save('temp.jpeg', "JPEG", quality=jpeg_quality)
img_reread = Image.open('temp.jpeg')
image = np.array(img_reread)
image = np.reshape(image, (img_rows, img_cols, img_chans))
# Generate adversarial images
adv_images[idx] = attack(image, y_test_c_ok[idx])
adversarial_image = 255 * adv_images[idx].reshape(
(img_rows, img_cols))
#######################################
#np.save('.................................' % idx,adversarial_image)
#path_adv_Image = '..................................'
#adversarial = adversarial_image
#cv2.imwrite(path_adv_Image + 'adv_%d.png' % idx, adversarial)
# Scores of legitimate and adversarial images for each idx
scoreTemp = fmodel.predictions(image)
true_score = foolbox.utils.softmax(scoreTemp)
true_class = np.argmax(true_score)
adv_score = foolbox.utils.softmax(
fmodel.predictions(adv_images[idx]))
adv_class = np.argmax(adv_score)
print(
'Image {}. Class changed from {} to {}. The score passes from {} to {}'
.format(idx, true_class, adv_class, true_score, adv_score))
'''print('After rounding. Class changed from {} to {}. The score passes from {} to {}'.format(idx, true_class,
Z_class, true_score,
Z_score))
'''
# the if below is to solve the strange problem with the prediction of a matrix of nan values...
if np.any(np.isnan(adv_images[idx])):
adv_class = true_class #attack not successful
t = t + 1
print('An adversarial image cannot be found!!')
if true_class == adv_class:
S = S + 1
# plot image, adv_image and difference
#Measure the distortion between the original image and attacked image
image_before = 255 * image.reshape((img_rows, img_cols))
diff = np.double(image_before) - np.double(adversarial_image)
#diff = np.double(image_before) - np.double(Z)
print(
'Max distortion adversarial [After Rounding] = {:3.4f}; L1 distortion = {:3.4f}'
.format(
abs(diff).max(),
abs(diff).sum() / (img_rows * img_cols)))
print('Percentage of modified pixels [After Rounding] = {:3.4f}'.
format(np.count_nonzero(diff) / (img_rows * img_cols)))
psnr_org = psnr(image_before, adversarial_image)
print('PSNR = {:3.4f}'.format(abs(psnr_org)))
X = np.uint8(image_before)
#Z = np.uint8(np.round(adversarial_image)) # Omit This Line Code
#show_figures(X,Z,true_score,Z_score)
# to save the result of the attack, save the Z matrix.......
#Z.save(...)
# update average distortion
if true_class != adv_class:
avg_Max_dist = avg_Max_dist + abs(diff).max()
avg_L1_dist = avg_L1_dist + abs(diff).sum() / (img_rows *
img_cols)
avg_No_Mod_Pixels = avg_No_Mod_Pixels + np.count_nonzero(
diff) / (img_rows * img_cols)
avg_psnr = avg_psnr + psnr(image_before, adversarial_image)
# -------------------------------
# #Compress JPEG the image and test again
# -------------------------------
'''if jpeg:
#cv2.imwrite('tmp.jpg', Z[::-1], [int(cv2.IMWRITE_JPEG_QUALITY), jpeg_quality])
#adv_reread = imread('tmp.jpg')
img1 = Image.fromarray(Z)
img1.save('temp.jpeg', "JPEG", quality= jpeg_quality)
adv_reread = Image.open('temp.jpeg')
x_test_comp = np.array(adv_reread)
x_test_comp = x_test_comp.reshape(img_rows, img_cols, img_chans)
x_test_comp = x_test_comp.astype('float32')
x_test_comp /= 255
adv_reread_score = foolbox.utils.softmax(fmodel.predictions(x_test_comp))
adv_reread_class = np.argmax(adv_reread_score)
if true_class == adv_reread_class:
S_jpg = S_jpg + 1
print('Class after JPEG compression {}, with score {}.'.format(adv_reread_class,adv_reread_score))
x_test_comp = 255* x_test_comp.reshape((img_rows, img_cols))
'''
n = n_test - S
print('Adversarial failures: {} over {}'.format(S, n_test))
print('Average distortion: max dist {}, L1 dist {}'.format(
avg_Max_dist / n, avg_L1_dist / n))
print('Average no of modified pixels: {}'.format(avg_No_Mod_Pixels /
n))
print('The adversarial image cannot be found {} times over {}'.format(
t, n_test))
if jpeg:
print(
'Percentage of adversarial JPEG unchanged with QF {} (the attack is not successful): {}'
.format(jpeg_quality, S_jpg / n_test))
# Evaluate accuracy
true_labels_cat = np.array(true_labels_cat)
adv_score = model.evaluate(adv_images, true_labels_cat, verbose=0)
#Z_score = model.evaluate(Z, true_labels_cat, verbose=0)
score_perfect = model.evaluate(x_test_ok, y_test_ok, verbose=0)
print('Accuracy on legitimate images (all): {:3.4f}'.format(score[1]))
print(
'Accuracy on legitimate images (only correctly classified, obviously 1): {:3.4f}'
.format(score_perfect[1]))
print('Accuracy on adversarial images: {:3.4f}'.format(adv_score[1]))
print('Attack success rate on adversarial images N1: {:3.4f}'.format(
1 - adv_score[1]))
print('Average PSNR =: {:3.4f}'.format(avg_psnr / n))
#print('Accuracy on legitimate images (all) by mismatched model: {:3.4f}'.format(score2[1]))
# SECOND PART
# Load the second model and test the adversarial images
# Label
label3 = 1 # it may be different from label because of the differences in the model.
# Labels
y_test_c = np.tile(label3, n_test)
# Convert labels to one-hot with Keras
y_test2 = keras.utils.to_categorical(y_test_c, num_classes)
#!/usr/bin/env python3 import keras from keras.applications.resnet50 import ResNet50 from foolbox.models import KerasModel from robust_vision_benchmark import imagenet_model_server keras.backend.set_learning_phase(0) kmodel = ResNet50(weights='imagenet') preprocessing = ([104, 116, 123], 1) fmodel = KerasModel(kmodel, bounds=(0, 255), preprocessing=preprocessing) imagenet_model_server(fmodel, channel_order='BGR', image_size=224)
from keras.applications.resnet50 import preprocess_input
from keras.applications.resnet50 import decode_predictions
from scipy.misc import imsave, imshow, imread
import matplotlib.pyplot as plt
import glob
<<<<<<< HEAD:NIPS_attack/test.py
image_dir = '/data0/images/imagenet12/imagenet224'
=======
image_dir = './imgs'
>>>>>>> e604fd2040b9fa9642b4f51892ca69633d9ac984:test.py
keras.backend.set_learning_phase(0)
kmodel = ResNet50(weights='imagenet')
preprocessing = (np.array([104, 116, 123]), 1)
fmodel = KerasModel(kmodel, bounds=(0, 255), preprocessing=preprocessing)
success = 0.
paths = glob.glob(image_dir+'/*.png')
<<<<<<< HEAD:NIPS_attack/test.py
print "{} images found".format(len(paths))
for path in paths[:100]:
=======
for path in paths:
>>>>>>> e604fd2040b9fa9642b4f51892ca69633d9ac984:test.py
image = imread(path).astype(np.float32)
test = image.copy()
preds = kmodel.predict(preprocess_input(np.expand_dims(test, 0)))
label = np.argmax(preds)
#print("Top 3 predictions (regular: ", decode_predictions(preds, top=3))
# Load two images. The cat image is original image
# and the dog image is used to initialize a targeted
# attack.
dog_img = image.load_img('dog.jpg', target_size=(224, 224))
cat_img = image.load_img('cat.jpg', target_size=(224, 224))
dog_img = image.img_to_array(dog_img)
cat_img = image.img_to_array(cat_img)
cat_img = 2.0 * cat_img / 255.0 - 1
dog_img = 2.0 * dog_img / 255.0 - 1
dog_x = np.expand_dims(dog_img, axis=0)
cat_x = np.expand_dims(cat_img, axis=0)
# Build a foolbox model
fmodel = KerasModel(kmodel, bounds=(-1, 1))
# label of the target class
preds = kmodel.predict(dog_x)
dog_label = np.argmax(preds)
# label of the original class
preds = kmodel.predict(cat_x)
cat_label = np.argmax(preds)
criterion_1 = TopKMisclassification(k=5)
criterion_2 = TargetClass(dog_label)
criterion_3 = TargetClassProbability(dog_label, p=0.5)
criterion = criterion_1 & criterion_2 & criterion_3
attack = BoundaryAttack(model=fmodel, criterion=criterion)
def main():
# Load Keras model
model = load_model(r'...........................h5') #First model
# Switch softmax with linear activations -- per evitare il softmax
Ptype = 'probabilities' #'logits' # 'probabilities'
# 64x64, 2 digits
img_rows, img_cols, img_chans = 128,128, 1
input_shape = (img_rows, img_cols, img_chans)
num_classes = 2
jpeg_quality = 85
jpeg = 0 # 'true'
compressJPEG = 0 #'true'
#---------------------------------------------------------
# Load test data and define labels (numImg, 64,64)
#-----------------------------------------------------------
images = glob(r'...................\*.png') #images from first model (Manipulated class)
label = 0 # label = 1 for Original and Label = 0 for Manipulated class
# mismatch model: Load Keras model
model2 = load_model(r'...................h5') #load second model
label2 = 1
# We compute accuracy based on numebr of images ( 5 ) #Ehsan
numImg = len(images)
np.random.seed(1234)
index = np.random.randint(len(images), size=numImg)
x_test = np.zeros((numImg, img_rows, img_cols))
for i in np.arange(numImg):
img = imread(images[index[i]], flatten=False) # Flatten=True means convert to gray on the fly
if compressJPEG:
img1 = Image.fromarray(img)
img1.save('temp.jpeg', "JPEG", quality=jpeg_quality)
img = Image.open('temp.jpeg')
x_test[i] = img
# Labels of authentic images = 1 (non-authentic = 0).
y_test_c = np.tile(label, numImg)
# Convert labels to one-hot with Keras
y_test = keras.utils.to_categorical(y_test_c, num_classes)
# Reshape test data, divide by 255 because net was trained this way
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, img_chans)
x_test = x_test.astype('float32')
x_test /= 255
# Test legitimate examples
score = model.evaluate(x_test, y_test, verbose=0)
#Returns the loss value (of the loss function) & metrics (accuracy ...) values for the model in test mode
predicted_legitimate_labels = np.argmax(model.predict(x_test), axis=1)
print('Accuracy on legitimate images (all): {:3.4f}'.format(score[1]))
y_test_c2 = np.tile(label2, numImg)
y_test2 = keras.utils.to_categorical(y_test_c2, num_classes)
#one-hot representation
score2 = model2.evaluate(x_test, y_test2, verbose=0)
# Returns the loss value (of the loss function) & metrics (accuracy ...) values for the model in test mode
#predicted_legitimate_labels2 = np.argmax(model2.predict(x_test), axis=1)
print('Accuracy on legitimate images (all) by mismatched model: {:3.4f}'.format(score2[1]))
# ----------------------------------------------------------------------------------------------------------------------
# Attack the first image of the test set
# ----------------------------------------------------------------------------------------------------------------------
# Wrap model
fmodel = KerasModel(model, bounds=(0, 1), predicts=Ptype)
# Prepare attack
#attack = foolbox.attacks.FGSM(fmodel)
#attack = foolbox.attacks.DeepFoolAttack(fmodel)
#attack = foolbox.attacks.DeepFoolAttack(fmodel)
#attack = foolbox.attacks.SaliencyMapAttack(fmodel,threshold=PSNR2MSE(55))
#attack = foolbox.attacks.LBFGSAttack(fmodel)
attack = foolbox.attacks.LBFGSAttack(fmodel, threshold=PSNR2MSE(55)) #LBFGS adversarial attack with limit PSNR
# ------Get data, labels and categorical labels ***only for correctly classified examples***
l = np.argwhere(predicted_legitimate_labels == y_test_c).shape[0]
x_test_ok = np.reshape(x_test[np.array(np.argwhere(predicted_legitimate_labels == y_test_c)), :, :, :], (l, img_rows,
img_cols,
img_chans))
test_ok_index = index[np.array(np.argwhere(predicted_legitimate_labels == y_test_c))]
# x_test_ok are the images that are correctly classified by the first model since we do not want to attack misclassified images
y_test_ok = np.reshape(y_test[np.argwhere(predicted_legitimate_labels == y_test_c), :], (l, num_classes))
y_test_c_ok = np.argmax(y_test_ok, axis=1)
y_test_c_ok_2 = np.tile(label2, l)
y_test_ok_2 = keras.utils.to_categorical(y_test_c_ok_2, num_classes)
score3 = model2.evaluate(x_test_ok, y_test_ok_2, verbose=0)
predicted_legitimate_labels2 = np.argmax(model2.predict(x_test_ok), axis=1)
l = np.argwhere(predicted_legitimate_labels2 == y_test_c_ok_2).shape[0]
x_test_ok = np.reshape(x_test_ok[np.array(np.argwhere(predicted_legitimate_labels2 == y_test_c_ok_2)), :, :, :],
(l, img_rows,
img_cols,
img_chans))
y_test_ok = np.reshape(y_test_ok[np.argwhere(predicted_legitimate_labels2 == y_test_c_ok_2), :],
(l, num_classes))
y_test_c_ok = np.argmax(y_test_ok, axis=1)
test_ok_index = np.squeeze(test_ok_index[np.array(np.argwhere(predicted_legitimate_labels2 == y_test_c_ok_2))])
# ------------------
# Elaborate n_test adversarial examples ***only for correctly classified examples***
n_test = l #Benedetta
#n_test = l #Ehsan : You're the man Ehsan
S = 0
S_int = 0
S_jpg = 0
avg_Max_dist = 0
avg_L1_dist = 0
avg_Max_dist_made_integer = 0
avg_L1_dist_made_integer = 0
avg_No_Mod_Pixels = 0
avg_No_Mod_Pixels_integer_rounding_adv_img = 0
avg_No_Mod_Pixels_integer_NO_rounding = 0
PSNR = 0
t = 0
avg_psnr = 0
avg_psnr_int = 0
psnr_org=0 #for each image
psnr_Int=0 #for each image
max_diff_integer=0
max_diff=0
adv_images = np.zeros((n_test, img_rows, img_cols, img_chans))
adv_images_integer = np.zeros((n_test, img_rows, img_cols, img_chans))
true_labels_cat = []
for idx in np.arange(n_test):
#n_test should be less than to the length of x_test_ok
image = x_test_ok[idx]
true_labels_cat.append(y_test_ok[idx, :])
image = image.astype('float32')
image_original = 255 * image.reshape((img_rows, img_cols))
if compressJPEG:
img1 = Image.fromarray(np.uint8(255*image[:,:,0]))
img1.save('temp.jpeg', "JPEG", quality=jpeg_quality)
img_reread = Image.open('temp.jpeg')
image = np.array(img_reread)
image = np.reshape(image, (img_rows, img_cols, img_chans))
# Generate adversarial images
adv_images[idx] = attack(image, y_test_c_ok[idx])
adversarial_image = 255 * adv_images[idx].reshape((img_rows, img_cols))
Z = np.uint8(np.round(adversarial_image))
# Store adversarial integer images
##############################################################################
path1='E:/......................./' #output folder
cv2.imwrite(os.path.join(path1, os.path.basename(images[test_ok_index[idx]])), Z)
##################################################################################
path2 = '''E:\..................\\'''
diff_noise=adversarial_image - image_original
Noise = np.uint8((diff_noise - np.min(diff_noise)) / (np.max(diff_noise) - np.min(diff_noise)))
cv2.imwrite(path2 + 'adv_Nosie_%d.png' % idx, 255*Noise)
adv_images_integer[idx] = np.reshape(Z / 255., (img_rows, img_cols, 1))
# Scores of legitimate and adversarial images for each idx
scoreTemp = fmodel.predictions(image)
true_score = foolbox.utils.softmax(scoreTemp)
true_class = np.argmax(true_score)
#it is the ground truth true_class according to network 1
adv_score = foolbox.utils.softmax(fmodel.predictions(adv_images[idx]))
adv_class = np.argmax(adv_score)
adv_integer_score = foolbox.utils.softmax(fmodel.predictions(adv_images_integer[idx]))
adv_integer_class = np.argmax(adv_integer_score)
print('Image {}. Class changed from {} to {}. The score passes from {} to {}'.format(idx, true_class,
adv_class, true_score,
adv_score))
print('Image Made Integer {}. Class changed from {} to {}. The score passes from {} to {}'.format(idx, true_class,
adv_integer_class, true_score,
adv_integer_score))
# the if below is to solve the strange problem with the prediction of a matrix of nan values...
if np.any(np.isnan(adv_images[idx])):
adv_class = true_class
adv_integer_class = true_class
t = t + 1
print('An adversarial image cannot be found!!')
if true_class == adv_class:
S = S+1
if true_class == adv_integer_class:
S_int = S_int + 1
# plot image, adv_image and difference
image_before = 255 * image.reshape((img_rows, img_cols))
X = np.uint8(image_before) # uint8 non ha effetto di troncamento
diff = np.double(image_before) - np.double(adversarial_image)
print('Max distortion adversarial = {:3.4f}; L1 distortion = {:3.4f}'.format(abs(diff).max(),
abs(diff).sum() / (
img_rows * img_cols)))
print('Percentage of modified pixels on integers = {:3.4f}. Percentage of negative modifications = {:3.4f}'.format(np.count_nonzero(diff)/(img_rows * img_cols), np.count_nonzero(np.double(abs(diff)) - np.double(diff))/(img_rows * img_cols)))
diff_integer = np.double(X) - np.double(Z)
max_diff_integer = diff_integer.max()
max_diff = diff.max()
path3 = '''E:\Benedetta_for_ICASSP\IMAGE_Diff_Int\\'''
Noise2 = np.uint8((diff_integer - np.min(diff_integer)) / (np.max(diff_integer) - np.min(diff_integer)))
cv2.imwrite(path3 + 'adv_Nosie_%d.png' % idx, 255 * Noise2)
print('Max distortion adversarial integer = {:3.4f}; L1 distortion = {:3.4f}'.format(abs(diff_integer).max(), abs(diff_integer).sum()/(img_rows * img_cols)))
#show_figures(X,Z,true_score,adv_score) #Ehsan: Compute PSNR for each Images org and Adversarial integer
psnr_org=psnr(image_before, adversarial_image)
print('PSNR = {:3.4f}'.format(abs(psnr_org)))
psnr_Int = psnr(X, Z)
print('PSNR (Integer) = {:3.4f}'.format(abs(psnr_Int)))
# update average distortion
if true_class != adv_class:
avg_Max_dist = avg_Max_dist + abs(diff).max()
avg_L1_dist = avg_L1_dist + abs(diff).sum()/(img_rows * img_cols)
avg_No_Mod_Pixels = avg_No_Mod_Pixels + np.count_nonzero(diff) / (img_rows * img_cols)
avg_psnr = avg_psnr + psnr(image_before, adversarial_image)
if true_class != adv_integer_class:
avg_Max_dist_made_integer = avg_Max_dist_made_integer + abs(diff_integer).max()
avg_L1_dist_made_integer = avg_L1_dist_made_integer + abs(diff_integer).sum()/(img_rows * img_cols)
avg_No_Mod_Pixels_integer_rounding_adv_img = avg_No_Mod_Pixels_integer_rounding_adv_img + np.count_nonzero(diff_integer) / (img_rows * img_cols) # ????????? why diff ????
#this after rounding to integer the adversarial image
avg_No_Mod_Pixels_integer_NO_rounding = avg_No_Mod_Pixels_integer_NO_rounding + np.count_nonzero(diff) / (img_rows * img_cols)
#this is just without rounding but counting the difference when the true class and the modified class are different
avg_psnr_int = avg_psnr_int + psnr(X, Z)
# -------------------------------
# #Compress JPEG the image and test again
# -------------------------------
if jpeg:
img1 = Image.fromarray(Z)
img1.save('temp.jpeg', "JPEG", quality= jpeg_quality)
adv_reread = Image.open('temp.jpeg')
x_test_comp = np.array(adv_reread)
x_test_comp = x_test_comp.reshape(img_rows, img_cols, img_chans)
x_test_comp = x_test_comp.astype('float32')
x_test_comp /= 255
adv_reread_score = foolbox.utils.softmax(fmodel.predictions(x_test_comp))
adv_reread_class = np.argmax(adv_reread_score)
if true_class == adv_reread_class:
S_jpg = S_jpg + 1
print('Class after JPEG compression {}, with score {}.'.format(adv_reread_class,adv_reread_score))
x_test_comp = 255* x_test_comp.reshape((img_rows, img_cols))
print('PSNR = {}'.format(psnr(image_before, x_test_comp)))
PSNR = psnr(image_before, x_test_comp) + PSNR
n=n_test-S
n_int=n_test-S_int
print('Class for the adversarial unchanged: {} over {}'.format(S,n_test))
# on how many test images (advesarial) the attack did not work
print('Class for the adversarial integer unchanged: {} over {}'.format(S_int,n_test))
# on how many test images (advesarial) integer the attack did not work
print('Average distortion: max dist {}, L1 dist {}'.format(avg_Max_dist/n,avg_L1_dist/n))
print('Average distortion (made integer): max dist {}, L1 dist {}'.format(avg_Max_dist_made_integer/n_int,avg_L1_dist_made_integer/n_int))
print('Average no of modified pixels: {}'.format(avg_No_Mod_Pixels/n))
print('Average no of modified pixels on integers NO ROUNDING: {}'.format(avg_No_Mod_Pixels_integer_NO_rounding /n_int))
print('Average no of modified pixels on integers rounding adv_img to int: {}'.format(avg_No_Mod_Pixels_integer_rounding_adv_img / n_int))
print('The adversarial image cannot be found {} times over {}'.format(t,n_test))
if jpeg:
print('Percentage of adversarial JPEG unchanged with QF {} (the attack is not successful): {}'.format(jpeg_quality, S_jpg/n_test))
print('Average PSNR distortion for JPEG adversarial images : {}'.format(PSNR/n_test))
# Evaluate accuracy
true_labels_cat = np.array(true_labels_cat)
adv_score = model.evaluate(adv_images, true_labels_cat, verbose=0)
adv_score_integer= model.evaluate(adv_images_integer, true_labels_cat, verbose=0)
score_perfect = model.evaluate(x_test_ok, y_test_ok, verbose=0)
print('Accuracy on legitimate images (all) by N1: {:3.4f}'.format(score[1]))
print('Accuracy on legitimate images (all) by mismatched model N2: {:3.4f}'.format(score2[1])) # ????? Score2
print('Accuracy on legitimate images (only correctly classified, obviously 1) N1: {:3.4f}'.format(score_perfect[1]))
print('Accuracy on adversarial images N1: {:3.4f}'.format(adv_score[1]))
print('Attack success rate on adversarial images N1: {:3.4f}'.format(1-adv_score[1]))
print('Accuracy on adversarial images (made integer) N1: {:3.4f}'.format(adv_score_integer[1]))
print('Attack success on adversarial images (made integer) N1: {:3.4f}'.format(1-adv_score_integer[1]))
print('Average PSNR =: {:3.4f}'.format(avg_psnr / n))
print('Average PSNR (Integer) =: {:3.4f}'.format(avg_psnr_int / n_int))
# SECOND PART
# Load the second model and test the adversarial images
# Label
#label3 = np.abs(1 - label2) # it may be different from label because of the differences in the model.
# Labels
y_test_c = np.tile(label2, n_test)
# Convert labels to one-hot with Keras
y_test2 = keras.utils.to_categorical(y_test_c, num_classes)
# Test
adv_score_mismatch = model2.evaluate(adv_images, y_test2, verbose=0)
# here Ehsan we need to evaluate model 2 in the same way but not on adv_images ... on adv_images_integer
adv_score_mismatch_on_integer = model2.evaluate(adv_images_integer, y_test2, verbose=0)
print('Accuracy on adversarial images with the mismatched model N2: {:3.4f}'.format(adv_score_mismatch[1]))
print('Attack success rate on adversarial images with the mismatched model N2: {:3.4f}'.format(1-adv_score_mismatch[1]))
print('Accuracy on adversarial images with the mismatched model (Integer) N2: {:3.4f}'.format(adv_score_mismatch_on_integer[1]))
print('Attack success rate on adversarial images with the mismatched model (Integer) N2: {:3.4f}'.format(1-adv_score_mismatch_on_integer[1]))
plot_sample = True
plot_samples = 100
# Define plot path.
if plot_sample:
path = './adv_samples/'
path += dataset
if not os.path.exists(path):
os.makedirs(path)
# -------------------------------------------------
# ADVERSARIAL SAMPLE GENERATION
# -------------------------------------------------
# Create Foolbox model from Keras ResNet classifier and FGSM attack type.
foolbox_model = KerasModel(model, (0, 1))
attack = FGSM(foolbox_model)
# Turn all test set samples into adversarial samples.
for i in tqdm(range(len(X_te))):
# Try to create an adversarial sample.
adv_sample = attack(np.reshape(X_te[i], orig_dims),
label=y_te[i],
max_epsilon=max_epsilon)
# In rare cases, sample generation might fail, which leaves adv_sample empty.
if adv_sample is not None:
# Successful adversarial samples are written back into the original matrix.
X_te[i] = np.reshape(adv_sample, np.prod(orig_dims))
from foolbox.criteria import Misclassification
import numpy as np
import keras
from keras.datasets import mnist
from keras.applications.resnet50 import ResNet50
from keras.applications.resnet50 import preprocess_input
from keras.applications.resnet50 import decode_predictions
from scipy.misc import imsave, imshow, imread
import matplotlib.pyplot as plt
import glob
from train_mnist import madry_mnist_model
keras.backend.set_learning_phase(0)
model = madry_mnist_model()
model.load_weights('mnist_madry.h5')
fmodel = KerasModel(model, bounds=(0, 1))
success = 0.
(images, labels), _ = mnist.load_data()
for image in images[:100]:
image = image.astype(np.float32) #[:, :, np.newaxis]/255.
image = image[np.newaxis, :, :, np.newaxis]
image /= 255.
test = image.copy()
preds = model.predict(test)
label = np.argmax(preds)
print("Label: ", label)
#imshow(image[0, :, :, 0])
from foolbox.criteria import ConfidentMisclassification import numpy as np import keras from keras.applications.resnet50 import ResNet50 from keras.applications.resnet50 import preprocess_input from keras.applications.resnet50 import decode_predictions import cv2 import scipy.io as sio import matplotlib.pyplot as plt # instantiate model keras.backend.set_learning_phase(0) kmodel = ResNet50(weights='imagenet') preprocessing = (np.array([103.0626,115.9029,123.1516]), 1) fmodel = KerasModel(kmodel, bounds=(0, 255), preprocessing=preprocessing) attacks=['GradientAttack', 'GradientSignAttack', 'IterativeGradientAttack', 'IterativeGradientSignAttack', 'LBFGSAttack', 'ApproximateLBFGSAttack', 'DeepFoolAttack', 'DeepFoolL2Attack', 'DeepFoolLinfinityAttack', 'SaliencyMapAttack', 'GaussianBlurAttack', 'ContrastReductionAttack', 'SinglePixelAttack',