摘要：本項目主要介紹了如何使用卷積神經網絡去檢測翻拍圖片，主要為摩爾紋圖片；其主要創新點在于網絡結構上，將圖片的高低頻信息分開處理。

1 前言

當感光元件像素的空間頻率與影像中條紋的空間頻率接近時，可能產生一種新的波浪形的干擾圖案，即所謂的摩爾紋。傳感器的網格狀紋理構成了一個這樣的圖案。當圖案中的細條狀結構與傳感器的結構以小角度交叉時，這種效應也會在圖像中產生明顯的干擾。這種現象在一些細密紋理情況下，比如時尚攝影中的布料上，非常普遍。這種摩爾紋可能通過亮度也可能通過顏色來展現。但是在這里，僅針對在翻拍過程中產生的圖像摩爾紋進行處理。

翻拍即從計算機屏幕上捕獲圖片，或對著屏幕拍攝圖片；該方式會在圖片上產生摩爾紋現象

論文主要處理思路

對原圖作 Haar 變換得到四個下采樣特征圖（原圖下二采樣 cA、Horizontal 橫向高頻 cH、Vertical 縱向高頻 cV、Diagonal 斜向高頻 cD）

然后分別利用四個獨立的 CNN 對四個下采樣特征圖卷積池化，提取特征信息

原文隨后對三個高頻信息卷積池化后的結果的每個 channel、每個像素點比對，取 max

將上一步得到的結果和 cA 卷積池化后的結果作笛卡爾積

2、網絡結構復現

如下圖所示，本項目復現了論文的圖像去摩爾紋方法，并對數據處理部分進行了修改，并且網絡結構上也參考了源碼中的結構，對圖片產生四個下采樣特征圖，而不是論文中的三個，具體處理方式大家可以參考一下網絡結構。

import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
# import pywt
from paddle.nn import Linear, Dropout, ReLU
from paddle.nn import Conv2D, MaxPool2D
class mcnn(nn.Layer):
 def __init__(self, num_classes=1000):
 super(mcnn, self).__init__()
 self.num_classes = num_classes
 self._conv1_LL = Conv2D(3,32,7,stride=2,padding=1,) 
 # self.bn1_LL = nn.BatchNorm2D(128)
 self._conv1_LH = Conv2D(3,32,7,stride=2,padding=1,) 
 # self.bn1_LH = nn.BatchNorm2D(256)
 self._conv1_HL = Conv2D(3,32,7,stride=2,padding=1,)
 # self.bn1_HL = nn.BatchNorm2D(512)
 self._conv1_HH = Conv2D(3,32,7,stride=2,padding=1,)
 # self.bn1_HH = nn.BatchNorm2D(256)
 self.pool_1_LL = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)
 self.pool_1_LH = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)
 self.pool_1_HL = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)
 self.pool_1_HH = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)
 self._conv2 = Conv2D(32,16,3,stride=2,padding=1,)
 self.pool_2 = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)
 self.dropout2 = Dropout(p=0.5)
 self._conv3 = Conv2D(16,32,3,stride=2,padding=1,)
 self.pool_3 = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)
 self._conv4 = Conv2D(32,32,3,stride=2,padding=1,)
 self.pool_4 = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)
 self.dropout4 = Dropout(p=0.5)
 # self.bn1_HH = nn.BatchNorm1D(256)
 self._fc1 = Linear(in_features=64,out_features=num_classes)
 self.dropout5 = Dropout(p=0.5)
 self._fc2 = Linear(in_features=2,out_features=num_classes)
 def forward(self, inputs1, inputs2, inputs3, inputs4):
        x1_LL = self._conv1_LL(inputs1)
        x1_LL = F.relu(x1_LL)
        x1_LH = self._conv1_LH(inputs2)
        x1_LH = F.relu(x1_LH)
        x1_HL = self._conv1_HL(inputs3)
        x1_HL = F.relu(x1_HL)
        x1_HH = self._conv1_HH(inputs4)
        x1_HH = F.relu(x1_HH)
        pool_x1_LL = self.pool_1_LL(x1_LL)
        pool_x1_LH = self.pool_1_LH(x1_LH)
        pool_x1_HL = self.pool_1_HL(x1_HL)
        pool_x1_HH = self.pool_1_HH(x1_HH)
        temp = paddle.maximum(pool_x1_LH, pool_x1_HL)
 avg_LH_HL_HH = paddle.maximum(temp, pool_x1_HH)
 inp_merged = paddle.multiply(pool_x1_LL, avg_LH_HL_HH)
        x2 = self._conv2(inp_merged)
        x2 = F.relu(x2)
        x2 = self.pool_2(x2)
        x2 = self.dropout2(x2)
        x3 = self._conv3(x2)
        x3 = F.relu(x3)
        x3 = self.pool_3(x3)
        x4 = self._conv4(x3)
        x4 = F.relu(x4)
        x4 = self.pool_4(x4)
        x4 = self.dropout4(x4)
        x4 = paddle.flatten(x4, start_axis=1, stop_axis=-1)
        x5 = self._fc1(x4)
        x5 = self.dropout5(x5)
        out = self._fc2(x5)
 return out
model_res = mcnn(num_classes=2)
paddle.summary(model_res,[(1,3,512,384),(1,3,512,384),(1,3,512,384),(1,3,512,384)])
---------------------------------------------------------------------------
 Layer (type)     Input Shape          Output Shape         Param #    
===========================================================================
   Conv2D-1 [[1, 3, 512, 384]] [1, 32, 254, 190] 4,736 
   Conv2D-2 [[1, 3, 512, 384]] [1, 32, 254, 190] 4,736 
   Conv2D-3 [[1, 3, 512, 384]] [1, 32, 254, 190] 4,736 
   Conv2D-4 [[1, 3, 512, 384]] [1, 32, 254, 190] 4,736 
  MaxPool2D-1 [[1, 32, 254, 190]] [1, 32, 127, 95] 0 
  MaxPool2D-2 [[1, 32, 254, 190]] [1, 32, 127, 95] 0 
  MaxPool2D-3 [[1, 32, 254, 190]] [1, 32, 127, 95] 0 
  MaxPool2D-4 [[1, 32, 254, 190]] [1, 32, 127, 95] 0 
   Conv2D-5 [[1, 32, 127, 95]] [1, 16, 64, 48] 4,624 
  MaxPool2D-5 [[1, 16, 64, 48]] [1, 16, 32, 24] 0 
   Dropout-1 [[1, 16, 32, 24]] [1, 16, 32, 24] 0 
   Conv2D-6 [[1, 16, 32, 24]] [1, 32, 16, 12] 4,640 
  MaxPool2D-6 [[1, 32, 16, 12]] [1, 32, 8, 6] 0 
   Conv2D-7 [[1, 32, 8, 6]] [1, 32, 4, 3] 9,248 
  MaxPool2D-7 [[1, 32, 4, 3]] [1, 32, 2, 1] 0 
   Dropout-2 [[1, 32, 2, 1]] [1, 32, 2, 1] 0 
   Linear-1 [[1, 64]] [1, 2] 130 
   Dropout-3 [[1, 2]] [1, 2] 0 
   Linear-2 [[1, 2]] [1, 2] 6 
===========================================================================
Total params: 37,592
Trainable params: 37,592
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 9.00
Forward/backward pass size (MB): 59.54
Params size (MB): 0.14
Estimated Total Size (MB): 68.68
---------------------------------------------------------------------------
{'total_params': 37592, 'trainable_params': 37592}

3、數據預處理

與源代碼不同的是，本項目將圖像的小波分解部分集成在了數據讀取部分，即改為了線上進行小波分解，而不是源代碼中的線下進行小波分解并且保存圖片。首先，定義小波分解的函數

!pip install PyWavelets
import numpy as np
import pywt
def splitFreqBands(img, levRows, levCols):
 halfRow = int(levRows/2)
 halfCol = int(levCols/2)
    LL = img[0:halfRow, 0:halfCol]
    LH = img[0:halfRow, halfCol:levCols]
    HL = img[halfRow:levRows, 0:halfCol]
    HH = img[halfRow:levRows, halfCol:levCols]
 return LL, LH, HL, HH
def haarDWT1D(data, length):
    avg0 = 0.5;
    avg1 = 0.5;
    dif0 = 0.5;
    dif1 = -0.5;
    temp = np.empty_like(data)
 # temp = temp.astype(float)
    temp = temp.astype(np.uint8)
    h = int(length/2)
 for i in range(h):
        k = i*2
        temp[i] = data[k] * avg0 + data[k + 1] * avg1;
 temp[i + h] = data[k] * dif0 + data[k + 1] * dif1;
 data[:] = temp
# computes the homography coefficients for PIL.Image.transform using point correspondences
def fwdHaarDWT2D(img):
 img = np.array(img)
 levRows = img.shape[0];
 levCols = img.shape[1];
 # img = img.astype(float)
 img = img.astype(np.uint8)
 for i in range(levRows):
        row = img[i,:]
        haarDWT1D(row, levCols)
 img[i,:] = row
 for j in range(levCols):
        col = img[:,j]
        haarDWT1D(col, levRows)
 img[:,j] = col
 return splitFreqBands(img, levRows, levCols)
!cd "data/data188843/" && unzip -q 'total_images.zip'
import os 
recapture_keys = [ 'ValidationMoire']
original_keys = ['ValidationClear']
def get_image_label_from_folder_name(folder_name):
 """
 :param folder_name:
 
    """
 for key in original_keys:
 if key in folder_name:
 return 'original'
 for key in recapture_keys:
 if key in folder_name:
 return 'recapture'
 return 'unclear'
label_name2label_id = {
 'original': 0,
 'recapture': 1,}
src_image_dir = "data/data188843/total_images"
dst_file = "data/data188843/total_images/train.txt"
image_folder = [file for file in os.listdir(src_image_dir)]
print(image_folder)
image_anno_list = []
for folder in image_folder:
 label_name = get_image_label_from_folder_name(folder)
 # label_id = label_name2label_id.get(label_name, 0)
 label_id = label_name2label_id[label_name]
 folder_path = os.path.join(src_image_dir, folder)
 image_file_list = [file for file in os.listdir(folder_path) if
 file.endswith('.jpg') or file.endswith('.jpeg') or
 file.endswith('.JPG') or file.endswith('.JPEG') or file.endswith('.png')]
 for image_file in image_file_list:
 # if need_root_dir:
 #     image_path = os.path.join(folder_path, image_file)
 # else:
 image_path = image_file
 image_anno_list.append(folder +"/"+image_path +"	"+ str(label_id) + '
')
dst_path = os.path.dirname(src_image_dir)
if not os.path.exists(dst_path):
 os.makedirs(dst_path)
with open(dst_file, 'w') as fd:
 fd.writelines(image_anno_list)
import paddle
import numpy as np
import pandas as pd
import PIL.Image as Image
from paddle.vision import transforms
# from haar2D import fwdHaarDWT2D
paddle.disable_static()
# 定義數據預處理
data_transforms = transforms.Compose([
 transforms.Resize(size=(448,448)),
 transforms.ToTensor(), # transpose操作 + (img / 255)
 # transforms.Normalize(      # 減均值 除標準差
 #     mean=[0.31169346, 0.25506335, 0.12432463],        
 #     std=[0.34042713, 0.29819837, 0.1375536])
 #計算過程：output[channel] = (input[channel] - mean[channel]) / std[channel]
])
# 構建Dataset
class MyDataset(paddle.io.Dataset):
 """
 步驟一：繼承paddle.io.Dataset類
    """
 def __init__(self, train_img_list, val_img_list, train_label_list, val_label_list, mode='train', ):
 """
 步驟二：實現構造函數，定義數據讀取方式，劃分訓練和測試數據集
        """
 super(MyDataset, self).__init__()
 self.img = []
 self.label = []
 # 借助pandas讀csv的庫
 self.train_images = train_img_list
 self.test_images = val_img_list
 self.train_label = train_label_list
 self.test_label = val_label_list
 if mode == 'train':
 # 讀train_images的數據
 for img,la in zip(self.train_images, self.train_label):
 self.img.append('/home/aistudio/data/data188843/total_images/'+img)
 self.label.append(paddle.to_tensor(int(la), dtype='int64'))
 else:
 # 讀test_images的數據
 for img,la in zip(self.test_images, self.test_label):
 self.img.append('/home/aistudio/data/data188843/total_images/'+img)
 self.label.append(paddle.to_tensor(int(la), dtype='int64'))
 def load_img(self, image_path):
 # 實際使用時使用Pillow相關庫進行圖片讀取即可，這里我們對數據先做個模擬
        image = Image.open(image_path).convert('RGB')
 # image = data_transforms(image)
 return image
 def __getitem__(self, index):
 """
 步驟三：實現__getitem__方法，定義指定index時如何獲取數據，并返回單條數據（訓練數據，對應的標簽）
        """
        image = self.load_img(self.img[index])
        LL, LH, HL, HH = fwdHaarDWT2D(image)
        label = self.label[index]
 # print(LL.shape)
 # print(LH.shape)
 # print(HL.shape)
 # print(HH.shape)
        LL = data_transforms(LL)
        LH = data_transforms(LH)
        HL = data_transforms(HL)
        HH = data_transforms(HH)
 print(type(LL))
 print(LL.dtype)
 return LL, LH, HL, HH, np.array(label, dtype='int64')
 def __len__(self):
 """
 步驟四：實現__len__方法，返回數據集總數目
        """
 return len(self.img)
image_file_txt = '/home/aistudio/data/data188843/total_images/train.txt'
with open(image_file_txt) as fd:
    lines = fd.readlines()
train_img_list = list()
train_label_list = list()
for line in lines:
 split_list = line.strip().split()
 image_name, label_id = split_list
 train_img_list.append(image_name)
 train_label_list.append(label_id)
# print(train_img_list)
# print(train_label_list)
# 測試定義的數據集
train_dataset = MyDataset(mode='train',train_label_list=train_label_list, train_img_list=train_img_list, val_img_list=train_img_list, val_label_list=train_label_list)
# test_dataset = MyDataset(mode='test')
# 構建訓練集數據加載器
train_loader = paddle.io.DataLoader(train_dataset, batch_size=2, shuffle=True)
# 構建測試集數據加載器
valid_loader = paddle.io.DataLoader(train_dataset, batch_size=2, shuffle=True)
print('=============train dataset=============')
for LL, LH, HL, HH, label in train_dataset:
 print('label: {}'.format(label))
 break

4、模型訓練

model2 = paddle.Model(model_res)
model2.prepare(optimizer=paddle.optimizer.Adam(parameters=model2.parameters()),
              loss=nn.CrossEntropyLoss(),
              metrics=paddle.metric.Accuracy())
model2.fit(train_loader,
 valid_loader,
        epochs=5,
        verbose=1,
 )

總結

本項目主要介紹了如何使用卷積神經網絡去檢測翻拍圖片，主要為摩爾紋圖片；其主要創新點在于網絡結構上，將圖片的高低頻信息分開處理。 在本項目中，CNN 僅使用 1 級小波分解進行訓練。 可以探索對多級小波分解網絡精度的影響。 CNN 模型可以用更多更難的例子和更深的網絡進行訓練。

審核編輯：劉清