Nelze spustit Carlini a Wagner Útoku pomocí foolbox na tensorflow Model

0

Otázka

Já používám nejnovější verzi foolbox (3.3.1), a můj kód jednoduše načíst RESNET-50 CNN, přidává některé vrstvy pro převedena učení aplikace, a zatížení váhy takto.

from numpy.core.records import array
import tensorflow as tf
from keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
import cv2
import os
import numpy as np
import foolbox as FB
from sklearn.metrics import accuracy_score
from scipy.spatial.distance import cityblock
from sklearn.metrics import plot_confusion_matrix
from sklearn.metrics import confusion_matrix
from PIL import Image
import foolbox as FB
import math
from foolbox.criteria import Misclassification

#load model
num_classes = 12

#Load model and prepare it for testing
print("Step 1: Load model and weights")
baseModel = ResNet50(weights=None, include_top=False, input_tensor=Input(shape=(224, 224, 3)))
headModel = baseModel.output
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(512, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(num_classes, activation="softmax")(headModel)
model = Model(inputs=baseModel.input, outputs=headModel)
model.load_weights("RESNET-50/weights/train1-test1.h5")

print("Step 2: prepare testing data")
#features is a set of (1200,10,224,224,3) images
features=np.load("features.npy")
labels=np.load("labels.npy")

Nyní bych chtěl zaútočit pomocí foolbox 3.3.1 Carlini a Wagner útoku, zde je způsob, jak načíst model pro foolbox

#Lets test the foolbox model
bounds = (0, 1)
fmodel = fb.TensorFlowModel(model, bounds=bounds)

Můj dataset je rozdělena do 10 snímků za dokument, jsem se útoku těchto 10 obrázků pomocí dávkové velikost 10 pro foolbox pomocí Carlini a Wagner útok

#for each i, I have 10 images
for i in range(0, features.shape[0]):

    print("document "+str(i))

    #Receive current values
    #This is a batch of (10,224,224,3) images
    features_to_test=features[i,:]
    #Get their labels
    labels_to_test=labels[i,:]

    ######################ATTACK IN THE NORMALIZED DOMAIN###########################  
    #lets do the attack
    #We use an interval of epsilons

    epsilons = np.linspace(0.01, 1, num=2)
    attack = fb.attacks.L2CarliniWagnerAttack(fmodel)
    adversarials = attack(features_to_test, labels_to_test, criterion=Misclassification(labels=labels_to_test), epsilons=epsilons)

Nicméně, když jsem spustit kód, tady je chyba, že se vrátil ke mně

Traceback (most recent call last):
File "test_carlini_wagner.py", line 161, in <module>
adversarials = attack(features_to_test, labels_to_test, 
criterion=Misclassification(labels=labels_to_test), epsilons=epsilons)
File "/usr/local/lib/python3.8/dist-packages/foolbox/attacks/base.py", line 410, in 
__call__
xp = self.run(model, x, criterion, early_stop=early_stop, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/foolbox/attacks/carlini_wagner.py", line 100, in run
bounds = model.bounds
AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute 
'bounds'

Co má být chyba? jsem načítání má model špatně? měl bych přidat nové parametry pro útok volal? jak již bylo dříve uvedeno, jsem na foolbox 3.3.1.

1

Nejlepší odpověď

1

Asi jste si popletl parametry L2CarliniWagnerAttack. Zde je zjednodušený funkční příklad s dummy data:

import tensorflow as tf
import numpy as np

from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from sklearn.metrics import accuracy_score
from scipy.spatial.distance import cityblock
from sklearn.metrics import plot_confusion_matrix
from sklearn.metrics import confusion_matrix
from foolbox import TensorFlowModel
from foolbox.criteria import Misclassification
from foolbox.attacks import L2CarliniWagnerAttack

num_classes = 12

print("Step 1: Load model and weights")
baseModel = ResNet50(weights=None, include_top=False, input_tensor=Input(shape=(224, 224, 3)))
headModel = baseModel.output
headModel = Flatten(name="flatten")(headModel)
headModel = Dense(512, activation="relu")(headModel)
headModel = Dropout(0.5)(headModel)
headModel = Dense(num_classes, activation="softmax")(headModel)
model = Model(inputs=baseModel.input, outputs=headModel)

bounds = (0, 1)
fmodel = TensorFlowModel(model, bounds=bounds)
images, labels = tf.random.normal((64, 10, 224, 224, 3)), tf.random.uniform((64, 10,), maxval=13, dtype=tf.int32)

for i in range(0, images.shape[0]):

    print("document "+str(i))
    features_to_test=images[i,:]
    labels_to_test=labels[i,:]

    epsilons = np.linspace(0.01, 1, num=2)
    attack = L2CarliniWagnerAttack()
    adversarials = attack(fmodel, features_to_test, criterion=Misclassification(labels_to_test), epsilons=epsilons)
Step 1: Load model and weights
document 0
document 1
document 2
document 3
document 4
document 5
document 6
...
2021-11-23 12:13:46

Díky za odpověď, funguje to! jedna otázka, proč tato metoda vyžaduje epsilons? zdá se, že přístup do jeho implementace není to ve výchozím nastavení. Ještě jednou díky.
mad

Jo, dobrá otázka.. docs se zdá být docela matoucí.
AloneTogether

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