我尝试使用深度学习来实现与水印有关的https://arxiv.org/abs/1810.07248代码，但是我需要在学习过程中应用高斯噪声之类的攻击。我使用了GaussianNoise层，但它说它仅在训练期间使用。因此，我很困惑，这意味着当我要测试我的网络时，此噪声层不起作用？如果我想使用高斯噪声层怎么办？我该如何实施？我还需要其他诸如裁剪的攻击，我不知道如何在层中实现它们：（（
from keras.layers import Input, Concatenate, GaussianNoise,Dropout,BatchNormalization
from keras.layers import Conv2D, AtrousConv2D
from keras.models import Model
from keras.datasets import mnist
from keras.callbacks import TensorBoard
from keras import backend as K
from keras import layers
import matplotlib.pyplot as plt
import tensorflow as tf
import keras as Kr
from keras.optimizers import SGD,RMSprop,Adam
from keras.callbacks import ReduceLROnPlateau
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
import numpy as np
import pylab as pl
import matplotlib.cm as cm
import keract
from matplotlib import pyplot
from keras import optimizers
from keras import regularizers
from tensorflow.python.keras.layers import Lambda;
#-----------------building w train---------------------------------------------
w_expand=np.zeros((49999,28,28),dtype='float32')
wv_expand=np.zeros((9999,28,28),dtype='float32')
wt_random=np.random.randint(2, size=(49999,4,4))
wt_random=wt_random.astype(np.float32)
wv_random=np.random.randint(2, size=(9999,4,4))
wv_random=wv_random.astype(np.float32)
w_expand[:,:4,:4]=wt_random
wv_expand[:,:4,:4]=wv_random
x,y,z=w_expand.shape
w_expand=w_expand.reshape((x,y,z,1))
x,y,z=wv_expand.shape
wv_expand=wv_expand.reshape((x,y,z,1))
#-----------------building w test---------------------------------------------
w_test = np.random.randint(2,size=(1,4,4))
w_test=w_test.astype(np.float32)
wt_expand=np.zeros((1,28,28),dtype='float32')
wt_expand[:,0:4,0:4]=w_test
wt_expand=wt_expand.reshape((1,28,28,1))
#-----------------------encoder------------------------------------------------
#------------------------------------------------------------------------------
wtm=Input((28,28,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e')(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e')(conv1)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e')(conv2)
#conv3 = Conv2D(8, (3, 3), activation='relu', padding='same', name='convl3e', kernel_initializer='Orthogonal',bias_initializer='glorot_uniform')(conv2)
BN=BatchNormalization()(conv3)
#DrO1=Dropout(0.25,name='Dro1')(BN)
encoded = Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I')(BN)
#-----------------------adding watermark---------------------------------------
#add_const = Kr.layers.Lambda(lambda x: x + Kr.backend.constant(w_expand))
#encoded_merged=keras.layers.Add()([encoded,wtm])
#add_const = Kr.layers.Lambda(lambda x: x + wtm)
#encoded_merged = add_const(encoded)
#encoder=Model(inputs=image, outputs= encoded_merged)
#encoded_merged = Concatenate(axis=3)([encoded, wtm])
add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
encoded_merged = add_const([encoded,wtm])
#encoder=Model(inputs=[image,wtm], outputs= encoded_merged ,name='encoder')
#encoder.summary()
#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
#deconv_input=Input((28,28,1),name='inputTodeconv')
#encoded_merged = Input((28, 28, 2))
deconv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1d')(encoded_merged)
deconv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2d')(deconv1)
deconv3 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl3d')(deconv2)
deconv4 = Conv2D(64, (5, 5), activation='relu',padding='same', name='convl4d')(deconv3)
BNd=BatchNormalization()(deconv3)
#DrO2=Dropout(0.25,name='DrO2')(BNd)
decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd)
#model=Model(inputs=image,outputs=decoded)
model=Model(inputs=[image,wtm],outputs=decoded)
decoded_noise = GaussianNoise(0.5)(decoded)
#----------------------w extraction------------------------------------
convw1 = Conv2D(16, (3,3), activation='relu', padding='same', name='conl1w')(decoded_noise)
convw2 = Conv2D(16, (3, 3), activation='relu', padding='same', name='convl2w')(convw1)
convw3 = Conv2D(16, (3, 3), activation='relu', padding='same', name='conl3w')(convw2)
convw4 = Conv2D(8, (3, 3), activation='relu', padding='same', name='conl4w')(convw3)
convw5 = Conv2D(8, (3, 3), activation='relu', padding='same', name='conl5w')(convw4)
convw6 = Conv2D(4, (3, 3), activation='relu', padding='same', name='conl6w')(convw5)
#BNed=BatchNormalization()(convw6)
#DrO3=Dropout(0.25, name='DrO3')(BNed)
pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W')(convw6)
# reconsider activation (is W positive?)
# should be filter=1 to match W
watermark_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])
watermark_extraction.summary()
#----------------------training the model--------------------------------------
#------------------------------------------------------------------------------
#----------------------Data preparation----------------------------------------
(x_train, _), (x_test, _) = mnist.load_data()
x_validation=x_train[1:10000,:,:]
x_train=x_train[10001:60000,:,:]
#
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_validation = x_validation.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) # adapt this if using `channels_first` image data format
x_validation = np.reshape(x_validation, (len(x_validation), 28, 28, 1))
#---------------------compile and train the model------------------------------
#opt=SGD(momentum=0.99)
watermark_extraction.compile(optimizer='adam', loss={'decoder_output':'mse','reconstructed_W':'binary_crossentropy'}, loss_weights={'decoder_output': 0.1, 'reconstructed_W': 1.0},metrics=['mae'])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=20)
#rlrp = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=20, min_delta=1E-4, verbose=1)
mc = ModelCheckpoint('best_model_5x5F_dp_gn_add_adam.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)
history=watermark_extraction.fit([x_train,w_expand], [x_train,w_expand],
epochs=200,
batch_size=32,
validation_data=([x_validation,wv_expand], [x_validation,wv_expand]),
callbacks=[TensorBoard(log_dir='E:/concatnatenetwork', histogram_freq=0, write_graph=False),es,mc])
watermark_extraction.summary()
WEIGHTS_FNAME = 'v1_adam_model_5x5F_add_dp_gn.hdf'
watermark_extraction.save_weights(WEIGHTS_FNAME, overwrite=True)
答案 0 :(得分：0)
我的错误是我也使用网络进行测试，但是我应该将网络分为两部分，然后使用具有高斯攻击的图像作为输入，而不是同一网络。具有相同结构和权重的两个新网络。