-
Notifications
You must be signed in to change notification settings - Fork 1
/
aae.py
214 lines (193 loc) · 9.08 KB
/
aae.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
from const import *
import tensorflow as tf
from tensorflow.keras.layers import Activation, Dense, BatchNormalization,\
Conv2D, Input, GaussianNoise, GlobalAveragePooling2D, Dropout
from tensorflow.keras.models import Model
from loss import *
from utils import *
import numpy as np
class HIDDEN():
# This is the class of the entire network
def __init__(self, height, width, channel, message_length, optimizer):
self.message_length = message_length # L on the paper
self.H = height # H on the paper
self.W = width # W on the paper
self.C = channel # C on the paper
self.image_shape = (self.H, self.W, self.C)
print("Build models...")
self._build_encoder_model()
self._build_noise_layer_model("identity")
self._build_decoder_model()
self._build_discriminator_model()
self._build_and_compile_network(optimizer)
def _build_encoder_model(self):
# Build the encoder
print("Building Encoder...")
input_images = Input(shape=self.image_shape, name='encoder_input')
input_messages = Input(shape=self.message_length,
name='input_messages')
# Phase 1
x = input_images
# Applying 4 Conv-BN-ReLU blocks with 64 output filters
for filters in [64, 64, 64, 64]:
x = Conv2D(filters=filters,
kernel_size=KERNEL_SIZE,
strides=1,
padding='same',
use_bias=False)(x)
x = BatchNormalization(-1)(x)
x = Activation("relu")(x)
# Phase 2
expanded_message = tf.expand_dims(input_messages, axis=1)
expanded_message = tf.expand_dims(expanded_message, axis=1)
a = tf.constant([1, self.H, self.W, 1], tf.int32)
expanded_message = tf.convert_to_tensor(
expanded_message, dtype=tf.float32)
# Replicating the message H*W times
expanded_message = tf.tile(expanded_message, a)
# Concatenate messages and images channel-wise
x2 = tf.concat([expanded_message, x, input_images], axis=-1)
# Phase 3
# Latest Conv-BN-ReLU block with 64 output filters
encoded_images = Conv2D(64,
kernel_size=KERNEL_SIZE,
strides=1,
padding='same',
use_bias=False)(x2)
encoded_images = BatchNormalization(-1)(encoded_images)
encoded_images = Activation("relu")(encoded_images)
# Final Convolutonial Layer with 1 x 1 kernel and C output filters
encoded_images = Conv2D(self.C, 1, padding='same',
strides=1)(encoded_images)
self.encoder_model = Model(
[input_images, input_messages], encoded_images, name='encoder')
def _build_noise_layer_model(self, name):
# Function that applies the noise layer to the image
print("Building Noise Layer...")
input_images = Input(shape=self.image_shape, name='noise_input')
if name == "identity":
self.noise_layer_model = Model(
input_images, input_images, name='noise')
elif name == "gaussian":
x = GaussianNoise(2)(input_images)
self.noise_layer_model = Model(input_images, x, name='noise')
elif name == "dropout":
x = Dropout(0.3)(input_images)
self.noise_layer_model = Model(input_images, x, name='noise')
def _build_decoder_model(self):
# Build the decoder
print("Building Decoder Generator...")
input_images = Input(shape=self.image_shape, name='decoder_input')
x = input_images
# Applying 7 Conv-BN-ReLU blocks with 64 output filters
for filters in [64, 64, 64, 64, 64, 64, 64]:
x = Conv2D(filters,
kernel_size=KERNEL_SIZE,
strides=1,
padding='same',
use_bias=False)(x)
x = BatchNormalization(axis=-1)(x)
x = Activation("relu")(x)
# Last ConvBNReLU with L filters
x = Conv2D(self.message_length,
kernel_size=KERNEL_SIZE,
padding='same',
use_bias=False)(x)
x = BatchNormalization(axis=-1)(x)
x = Activation("relu")(x)
# Average Pooling over all spatial dimensions
x = GlobalAveragePooling2D()(x)
# Final linear layer with L units
x = Dense(self.message_length)(x)
self.decoder_model = Model(input_images, x, name='decoder')
def _build_discriminator_model(self):
# build the adversary
input_images = Input(shape=self.image_shape, name='adversary_input')
x = input_images
# Applying 3 Conv-BN-ReLU blocks with 64 output filters
for filters in [64, 64, 64]:
x = Conv2D(filters,
kernel_size=KERNEL_SIZE,
strides=1,
padding='same')(x)
x = BatchNormalization(axis=-1)(x)
x = Activation("relu")(x)
# Average Pooling over all spatial dimensions
x = GlobalAveragePooling2D()(x)
# Final linear layer to classify the image
adversary_output = Dense(2, activation="softmax")(x)
self.discriminator_model = Model(
input_images, adversary_output, name='discriminator')
def _build_and_compile_network(self, optimizer):
self.discriminator_model.compile(
loss=discriminator_loss, optimizer="adam")
# We will only train the Encoder and the Decoder
self.discriminator_model.trainable = False
print("Conecting models...")
images = Input(shape=self.image_shape, name='input')
messages = Input(shape=self.message_length, name='messages')
encoder_output = self.encoder_model([images, messages])
noise_output = self.noise_layer_model(encoder_output)
decoder_output = self.decoder_model(noise_output)
discriminator_output = self.discriminator_model(encoder_output)
# The final network: Encoder + Noise + Decoder + Adversary
self.network = Model([images, messages], [
noise_output, decoder_output, discriminator_output], name='hidden')
# Compile all the network
self.network.compile(loss=["mse", message_distortion_loss, adversary_loss],
# The relative weights of the losses, lambda_i and lambda_g
loss_weights=[0.7, 1, 0.001],
optimizer=optimizer)
# Train on batch the entire network
def train(self, epochs, train_images, train_messages):
for epoch in range(epochs + 1):
batch, _ = next(train_images)
batch_size = len(batch)
index = np.random.randint(0, len(train_images), batch_size)
real = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
batch_messages = train_messages[index]
cover_images = batch
encoded_images = self.encoder_model.predict(
[batch, batch_messages])
# Train the adversary
loss_real = self.discriminator_model.train_on_batch(
cover_images, real)
loss_fake = self.discriminator_model.train_on_batch(
encoded_images, fake)
# Train all the network
autoencoder_loss = self.network.train_on_batch(
[batch, batch_messages], [batch, batch_messages, real])
print(
f"Epoch {epoch} Autoencoder loss: {autoencoder_loss[0]}\
Image loss: {autoencoder_loss[1]}\
Message loss: {autoencoder_loss[2]},\
Adversary loss: {autoencoder_loss[3]}")
# Predict on batch
def predict(self, prediction_images, prediction_messages, plain_msg, index):
print("Starting Prediction")
decoded_img = []
decoded_msg = []
x = prediction_images
for i in range(len(x)):
batch, _ = next(x)
batch_size = len(batch)
pred_messages = prediction_messages[i *
batch_size:i*batch_size + batch_size]
(imgs, msgs, _) = self.network.predict_on_batch(
[batch, pred_messages])
for img in imgs:
decoded_img.append(img)
for msg in msgs:
decoded_msg.append(msg)
self.decoded_img = decoded_img
self.decoded_msg = decoded_msg
predicted_message = round_predicted_message(decoded_msg[index])
original_message = round_predicted_message(prediction_messages[index])
print("Original message as String: ", plain_msg[index])
print("Original message in Binary: ", original_message)
print("Predicted message in Binary: ", predicted_message)
errors = count_errors(original_message, predicted_message)
print(f'Errors {errors}/{self.message_length}')
def save(self, path):
self.network.save(path)