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"""
This file defines all BusterNet related custom layers
"""
from __future__ import print_function
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Layer, Input, Lambda
from tensorflow.keras.layers import BatchNormalization, Activation, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras import backend as K
import tensorflow as tf
def std_norm_along_chs(x):
"""Data normalization along the channle axis
Input:
x = tensor4d, (n_samples, n_rows, n_cols, n_feats)
Output:
xn = tensor4d, same shape as x, normalized version of x
"""
avg = K.mean(x, axis=-1, keepdims=True)
std = K.maximum(1e-4, K.std(x, axis=-1, keepdims=True))
return (x - avg) / std
def BnInception(x, nb_inc=16, inc_filt_list=[(1, 1), (3, 3), (5, 5)], name="uinc"):
"""Basic Google inception module with batch normalization
Input:
x = tensor4d, (n_samples, n_rows, n_cols, n_feats)
nb_inc = int, number of filters in individual Conv2D
inc_filt_list = list of kernel sizes, individual Conv2D kernel size
name = str, name of module
Output:
xn = tensor4d, (n_samples, n_rows, n_cols, n_new_feats)
"""
uc_list = []
for idx, ftuple in enumerate(inc_filt_list):
uc = Conv2D(
nb_inc,
ftuple,
activation="linear",
padding="same",
name=name + "_c%d" % idx,
)(x)
uc_list.append(uc)
if len(uc_list) > 1:
uc_merge = Concatenate(axis=-1, name=name + "_merge")(uc_list)
else:
uc_merge = uc_list[0]
uc_norm = BatchNormalization(name=name + "_bn")(uc_merge)
xn = Activation("relu", name=name + "_re")(uc_norm)
return xn
class SelfCorrelationPercPooling(Layer):
"""Custom Self-Correlation Percentile Pooling Layer
Arugment:
nb_pools = int, number of percentile poolings
Input:
x = tensor4d, (n_samples, n_rows, n_cols, n_feats)
Output:
x_pool = tensor4d, (n_samples, n_rows, n_cols, nb_pools)
"""
def __init__(self, nb_pools=256, **kwargs):
self.nb_pools = nb_pools
super(SelfCorrelationPercPooling, self).__init__(**kwargs)
def build(self, input_shape):
self.built = True
def call(self, x, mask=None):
# parse input feature shape
bsize, nb_rows, nb_cols, nb_feats = K.int_shape(x)
nb_maps = nb_rows * nb_cols
# self correlation
x_3d = K.reshape(x, tf.stack([-1, nb_maps, nb_feats]))
x_corr_3d = (
tf.matmul(x_3d, x_3d, transpose_a=False, transpose_b=True) / nb_feats
)
x_corr = K.reshape(x_corr_3d, tf.stack([-1, nb_rows, nb_cols, nb_maps]))
# argsort response maps along the translaton dimension
if self.nb_pools is not None:
ranks = K.cast(
K.round(tf.linspace(1.0, nb_maps - 1, self.nb_pools)), "int32"
)
else:
ranks = tf.range(1, nb_maps, dtype="int32")
x_sort, _ = tf.nn.top_k(x_corr, k=nb_maps, sorted=True)
# pool out x features at interested ranks
# NOTE: tf v1.1 only support indexing at the 1st dimension
x_f1st_sort = K.permute_dimensions(x_sort, (3, 0, 1, 2))
x_f1st_pool = tf.gather(x_f1st_sort, ranks)
x_pool = K.permute_dimensions(x_f1st_pool, (1, 2, 3, 0))
return x_pool
def compute_output_shape(self, input_shape):
bsize, nb_rows, nb_cols, nb_feats = input_shape
nb_pools = (
self.nb_pools if (self.nb_pools is not None) else (nb_rows * nb_cols - 1)
)
return tuple([bsize, nb_rows, nb_cols, nb_pools])
class BilinearUpSampling2D(Layer):
"""Custom 2x bilinear upsampling layer
Input:
x = tensor4d, (n_samples, n_rows, n_cols, n_feats)
Output:
x2 = tensor4d, (n_samples, 2*n_rows, 2*n_cols, n_feats)
"""
def call(self, x, mask=None):
bsize, nb_rows, nb_cols, nb_filts = K.int_shape(x)
new_size = tf.constant([nb_rows * 2, nb_cols * 2], dtype=tf.int32)
return tf.image.resize(x, new_size)
def compute_output_shape(self, input_shape):
bsize, nb_rows, nb_cols, nb_filts = input_shape
return tuple([bsize, nb_rows * 2, nb_cols * 2, nb_filts])
class ResizeBack(Layer):
"""Custom bilinear resize layer
Resize x's spatial dimension to that of r
Input:
x = tensor4d, (n_samples, n_rowsX, n_colsX, n_featsX )
r = tensor4d, (n_samples, n_rowsR, n_colsR, n_featsR )
Output:
xn = tensor4d, (n_samples, n_rowsR, n_colsR, n_featsX )
"""
def call(self, x):
t, r = x
new_size = [tf.shape(r)[1], tf.shape(r)[2]]
return tf.image.resize(t, new_size)
def compute_output_shape(self, input_shapes):
tshape, rshape = input_shapes
return (tshape[0],) + rshape[1:3] + (tshape[-1],)
class Preprocess(Layer):
"""Basic preprocess layer for BusterNet
More precisely, it does the following two things
1) normalize input image size to (256,256) to speed up processing
2) substract channel-wise means if necessary
"""
def call(self, x, mask=None):
# parse input image shape
bsize, nb_rows, nb_cols, nb_colors = K.int_shape(x)
if (nb_rows != 256) or (nb_cols != 256):
# resize image if different from (256,256)
x256 = tf.image.resize(x, [256, 256], name="resize")
else:
x256 = x
# substract channel means if necessary
if K.dtype(x) == "float32":
# input is not a 'uint8' image
# assume it has already been normalized
xout = x256
else:
# input is a 'uint8' image
# substract channel-wise means
xout = preprocess_input(x256)
return xout
def compute_output_shape(self, input_shape):
return (input_shape[0], 256, 256, 3)
def create_cmfd_similarity_branch(
img_shape=(256, 256, 3), nb_pools=100, name="simiDet"
):
"""Create the similarity branch for copy-move forgery detection"""
# ---------------------------------------------------------
# Input
# ---------------------------------------------------------
img_input = Input(shape=img_shape, name=name + "_in")
# ---------------------------------------------------------
# VGG16 Conv Featex
# ---------------------------------------------------------
bname = name + "_cnn"
## Block 1
x1 = Conv2D(64, (3, 3), activation="relu", padding="same", name=bname + "_b1c1")(
img_input
)
x1 = Conv2D(64, (3, 3), activation="relu", padding="same", name=bname + "_b1c2")(x1)
x1 = MaxPooling2D((2, 2), strides=(2, 2), name=bname + "_b1p")(x1)
# Block 2
x2 = Conv2D(128, (3, 3), activation="relu", padding="same", name=bname + "_b2c1")(
x1
)
x2 = Conv2D(128, (3, 3), activation="relu", padding="same", name=bname + "_b2c2")(
x2
)
x2 = MaxPooling2D((2, 2), strides=(2, 2), name=bname + "_b2p")(x2)
# Block 3
x3 = Conv2D(256, (3, 3), activation="relu", padding="same", name=bname + "_b3c1")(
x2
)
x3 = Conv2D(256, (3, 3), activation="relu", padding="same", name=bname + "_b3c2")(
x3
)
x3 = Conv2D(256, (3, 3), activation="relu", padding="same", name=bname + "_b3c3")(
x3
)
x3 = MaxPooling2D((2, 2), strides=(2, 2), name=bname + "_b3p")(x3)
# Block 4
x4 = Conv2D(512, (3, 3), activation="relu", padding="same", name=bname + "_b4c1")(
x3
)
x4 = Conv2D(512, (3, 3), activation="relu", padding="same", name=bname + "_b4c2")(
x4
)
x4 = Conv2D(512, (3, 3), activation="relu", padding="same", name=bname + "_b4c3")(
x4
)
x4 = MaxPooling2D((2, 2), strides=(2, 2), name=bname + "_b4p")(x4)
# Local Std-Norm Normalization (within each sample)
xx = Activation(std_norm_along_chs, name=bname + "_sn")(x4)
# ---------------------------------------------------------
# Self Correlation Pooling
# ---------------------------------------------------------
bname = name + "_corr"
## Self Correlation
xcorr = SelfCorrelationPercPooling(name=bname + "_corr")(xx)
## Global Batch Normalization (across samples)
xn = BatchNormalization(name=bname + "_bn")(xcorr)
# ---------------------------------------------------------
# Deconvolution Network
# ---------------------------------------------------------
patch_list = [(1, 1), (3, 3), (5, 5)]
# MultiPatch Featex
bname = name + "_dconv"
f16 = BnInception(xn, 8, patch_list, name=bname + "_mpf")
# Deconv x2
f32 = BilinearUpSampling2D(name=bname + "_bx2")(f16)
dx32 = BnInception(f32, 6, patch_list, name=bname + "_dx2")
# Deconv x4
f64a = BilinearUpSampling2D(name=bname + "_bx4a")(f32)
f64b = BilinearUpSampling2D(name=bname + "_bx4b")(dx32)
f64 = Concatenate(axis=-1, name=name + "_dx4_m")([f64a, f64b])
dx64 = BnInception(f64, 4, patch_list, name=bname + "_dx4")
# Deconv x8
f128a = BilinearUpSampling2D(name=bname + "_bx8a")(f64a)
f128b = BilinearUpSampling2D(name=bname + "_bx8b")(dx64)
f128 = Concatenate(axis=-1, name=name + "_dx8_m")([f128a, f128b])
dx128 = BnInception(f128, 2, patch_list, name=bname + "_dx8")
# Deconv x16
f256a = BilinearUpSampling2D(name=bname + "_bx16a")(f128a)
f256b = BilinearUpSampling2D(name=bname + "_bx16b")(dx128)
f256 = Concatenate(axis=-1, name=name + "_dx16_m")([f256a, f256b])
dx256 = BnInception(f256, 2, patch_list, name=bname + "_dx16")
# Summerize
fm256 = Concatenate(axis=-1, name=name + "_mfeat")([f256a, dx256])
masks = BnInception(fm256, 2, [(5, 5), (7, 7), (11, 11)], name=bname + "_dxF")
# ---------------------------------------------------------
# Output for Auxiliary Task
# ---------------------------------------------------------
pred_mask = Conv2D(
1, (3, 3), activation="sigmoid", name=name + "_pred_mask", padding="same"
)(masks)
# ---------------------------------------------------------
# End to End
# ---------------------------------------------------------
model = Model(inputs=img_input, outputs=pred_mask, name=name)
return model
def create_cmfd_manipulation_branch(img_shape=(256, 256, 3), name="maniDet"):
"""Create the manipulation branch for copy-move forgery detection"""
# ---------------------------------------------------------
# Input
# ---------------------------------------------------------
img_input = Input(shape=img_shape, name=name + "_in")
# ---------------------------------------------------------
# VGG16 Conv Featex
# ---------------------------------------------------------
bname = name + "_cnn"
# Block 1
x1 = Conv2D(64, (3, 3), activation="relu", padding="same", name=bname + "_b1c1")(
img_input
)
x1 = Conv2D(64, (3, 3), activation="relu", padding="same", name=bname + "_b1c2")(x1)
x1 = MaxPooling2D((2, 2), strides=(2, 2), name=bname + "_b1p")(x1)
# Block 2
x2 = Conv2D(128, (3, 3), activation="relu", padding="same", name=bname + "_b2c1")(
x1
)
x2 = Conv2D(128, (3, 3), activation="relu", padding="same", name=bname + "_b2c2")(
x2
)
x2 = MaxPooling2D((2, 2), strides=(2, 2), name=bname + "_b2p")(x2)
# Block 3
x3 = Conv2D(256, (3, 3), activation="relu", padding="same", name=bname + "_b3c1")(
x2
)
x3 = Conv2D(256, (3, 3), activation="relu", padding="same", name=bname + "_b3c2")(
x3
)
x3 = Conv2D(256, (3, 3), activation="relu", padding="same", name=bname + "_b3c3")(
x3
)
x3 = MaxPooling2D((2, 2), strides=(2, 2), name=bname + "_b3p")(x3)
# Block 4
x4 = Conv2D(512, (3, 3), activation="relu", padding="same", name=bname + "_b4c1")(
x3
)
x4 = Conv2D(512, (3, 3), activation="relu", padding="same", name=bname + "_b4c2")(
x4
)
x4 = Conv2D(512, (3, 3), activation="relu", padding="same", name=bname + "_b4c3")(
x4
)
x4 = MaxPooling2D((2, 2), strides=(2, 2), name=bname + "_b4p")(x4)
# ---------------------------------------------------------
# Deconvolution Network
# ---------------------------------------------------------
patch_list = [(1, 1), (3, 3), (5, 5)]
bname = name + "_dconv"
# MultiPatch Featex
f16 = BnInception(x4, 8, patch_list, name=bname + "_mpf")
# Deconv x2
f32 = BilinearUpSampling2D(name=bname + "_bx2")(f16)
dx32 = BnInception(f32, 6, patch_list, name=bname + "_dx2")
# Deconv x4
f64 = BilinearUpSampling2D(name=bname + "_bx4")(dx32)
dx64 = BnInception(f64, 4, patch_list, name=bname + "_dx4")
# Deconv x8
f128 = BilinearUpSampling2D(name=bname + "_bx8")(dx64)
dx128 = BnInception(f128, 2, patch_list, name=bname + "_dx8")
# Deconv x16
f256 = BilinearUpSampling2D(name=bname + "_bx16")(dx128)
dx256 = BnInception(f256, 2, [(5, 5), (7, 7), (11, 11)], name=bname + "_dx16")
# ---------------------------------------------------------
# Output for Auxiliary Task
# ---------------------------------------------------------
pred_mask = Conv2D(
1, (3, 3), activation="sigmoid", name=bname + "_pred_mask", padding="same"
)(dx256)
# ---------------------------------------------------------
# End to End
# ---------------------------------------------------------
model = Model(inputs=img_input, outputs=pred_mask, name=bname)
return model
def create_BusterNet_testing_model(weight_file=None):
"""create a busterNet testing model with pretrained weights"""
# 1. create branch model
simi_branch = create_cmfd_similarity_branch()
mani_branch = create_cmfd_manipulation_branch()
# 2. crop off the last auxiliary task layer
SimiDet = Model(
inputs=simi_branch.inputs,
outputs=simi_branch.layers[-2].output,
name="simiFeatex",
)
ManiDet = Model(
inputs=mani_branch.inputs,
outputs=mani_branch.layers[-2].output,
name="maniFeatex",
)
# 3. define the two-branch BusterNet model
# 3.a define wrapper inputs
img_raw = Input(shape=(None, None, 3), name="image_in")
img_in = Preprocess(name="preprocess")(img_raw)
# 3.b define BusterNet Core
simi_feat = SimiDet(img_in)
mani_feat = ManiDet(img_in)
merged_feat = Concatenate(axis=-1, name="merge")([simi_feat, mani_feat])
f = BnInception(merged_feat, 3, name="fusion")
mask_out = Conv2D(
3, (3, 3), padding="same", activation="softmax", name="pred_mask"
)(f)
# 3.c define wrapper output
mask_out = ResizeBack(name="restore")([mask_out, img_raw])
# 4. create BusterNet model end-to-end
model = Model(inputs=img_raw, outputs=mask_out, name="busterNet")
if weight_file is not None:
try:
model.load_weights(weight_file)
print(
"INFO: successfully load pretrained weights from {}".format(weight_file)
)
except Exception as e:
print(
"INFO: fail to load pretrained weights from {} for reason: {}".format(
weight_file, e
)
)
return model
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