Eu estou usando a versão mais recente do foolbox (3.3.1), e o meu código simplesmente carregar um RESNET-50 CNN, adiciona algumas camadas para um transferidos de aprendizagem, aplicação e carrega os pesos da seguinte forma.
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")
Agora eu gostaria de atacá-lo usando o foolbox 3.3.1 Carlini e Wagner de ataque, aqui é a minha maneira de carregar o modelo para foolbox
#Lets test the foolbox model
bounds = (0, 1)
fmodel = fb.TensorFlowModel(model, bounds=bounds)
Meu conjunto de dados é dividido em 10 imagens por documento, eu vou atacar essas 10 imagens usando um tamanho de lote de 10 para foolbox usando Carlini e Wagner ataque
#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)
No entanto, quando eu executar o código, aqui é o erro é retornado para mim
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'
O que é suposto ser o erro? eu estou carregando o meu modelo indevidamente? eu deveria adicionar novos parâmetros para o ataque chamado? como dito anteriormente, estou em foolbox 3.3.1.