图像篡改检测 python_GitHub - Allenem/graduation-design: 本科毕业设计：针对Deepfake假脸视频面部细节特征的提取算法...
针对Deepfake假脸视频面部细节特征的提取算法
目录
一、仓库说明
.
│ LICENSE # 许可说明
│ README.md # 简介
│
├─References # 参考文献
├─FeatureExtractionLearning # 学习特征提取 代码文件夹
├─DatabasePreprocessing # 数据库预处理：图片提取人脸，视频分帧存图提取人脸
├─DatabaseFeatureExtraction # 提取 Celeba PGGAN DFD 数据集人脸特征 代码文件夹
├─SVM # 学习SVM分类器，分类器实现人脸判别 代码文件夹
├─screenshots # 一些截图
└─Paper # 我的论文
二、工作计划
1.数据库分配
学生
真脸
GAN假脸数据库
Deepfake数据库
JYT
FFHQ(0-35000)
styleGAN2
TIMIT
XJ
Celeba(train)
styleGAN
DFDC
ZS
FFHQ(35001-70000)
starGAN
faceforensic
PY
Celeba(validation,test)
PGGAN
DeepfakeDetection
2.特征分配
学生
特征
JYT
1、局部二值模式LBP 2、方向梯度直方图HOG 3、SRM残差图像
XJ
1、共生矩阵 2、光流场 3、LPQ特征
ZS
1、直方图/共生矩阵 2、拉普拉斯变换均方差 3、小波变换频率直方图
PY
1、颜色直方图 2、Surf 3、错误级别分析(Error level analysis，ELA)
3.工作计划
起止时间
工作内容
备注
2020.01-2020.02
调研和资料分析
2020.01-2020.02
数据库预处理
视频分帧、人脸提取及定位
2020.02-2020.03
提取人脸特征、检测GAN真假脸图像差异
隐写分析特征或者图像篡改特征
2020.03-2020.04
Deepfake换脸视频检测算法实现
SVM分类器等不同分类器
2020.04-2020.05
完成毕业论文
三、调研和资料分析
1.参考文献
……
2.我自己的中文翻译
3.Python学习和人脸检测学习
OpenCV，dlib，face_recognition 实现人脸检测，标志检测等实验小测试： https://github.com/Allenem/PyTest
四、学习特征提取
1、颜色直方图
1.1 matplotlib画图像变色问题
在使用opencv配合jupyter notebook调试，其中常常使用matplotlib来显示图像
import cv2
import numpy as np
import matplotlib.pyplot as plt
image = cv2.imread("image.jpg")
# 后面的方法都从此处开始更改
plt.subplot(),plt.imshow(image),plt.title('Input')
plt.show()
但是在实际使用过程中，我们会发现plt.imshow()后出现的图形变成了负片，这是因为cv2.imshow()与plt.imshow()的通道顺序不同产生的，前者BGR，后者RGB。
解决方法一：
b, g, r = cv2.split(image)
image_new = cv2.merge([r, g, b])
plt.subplot(),plt.imshow(image_new),plt.title('Input')
plt.show()
解决方法二：
image_new = np.flip(original_img,axis = 2)
plt.subplot(),plt.imshow(image_new),plt.title('Input')
plt.show()
解决方法三：
image_new = image[:,:,::-1]
plt.subplot(),plt.imshow(image_new),plt.title('Input')
plt.show()
当然cv2自己显示没有问题，它的颜色顺序是BGR
import cv2
import numpy as np
image = cv2.imread("image.jpg")
cv2.imshow("Img", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
1.2 histogram.py 和 histogram3lines.py 效果图
2、Surf
2.1 SURF简介
SURF(Speeded Up Robust Features) 加速鲁棒特征。正如其名，它是加速版本的 SIFT(Scale-invariant feature transform) 尺度不变特征转换。
它善于处理具有模糊和旋转的图像，但是不善于处理视角变化和光照变化。在SIFT中使用高斯微分 DoG(Difference of Gaussian) 对高斯拉普拉斯算子 LoG(Laplacian of Gaussian) 进行近似，而在SURF中使用盒子滤波器 Box Filter 对 LoG 进行近似，这样就可以使用积分图像了(计算图像中某个窗口内所有像素和时，计算量的大小与窗口大小无关)。总之，SURF最大的特点在于采用了 Haar特征 以及 积分图像 的概念，大大加快了程序的运行效率。
2.2 SURF小实验和效果图
① 创建一个SURF对象
cv2.xfeatures2d.SURF_create(, hessianThreshold, nOctaves, nOctaveLayers, extended, upright)
hessianThreshold：默认100，关键点检测的阈值，越高监测的点越少
nOctaves：默认4，金字塔组数
nOctaveLayers：默认3，每组金子塔的层数
extended：默认False，扩展描述符标志，True表示使用扩展的128个元素描述符，False表示使用64个元素描述符。
upright：默认False，垂直向上或旋转的特征标志，True表示不计算特征的方向，False-计算方向。
之后也可以通过类似getHessianThreshold()，setHessianThreshold()等函数来获取或修改上述参数值，例如
surf.setHessianThreshold(True) 表示将HessianThreshold参数修改为True。
② 绘制特征点
cv2.drawKeypoint(image, keypoints, outImage, color, flags)
或：
outImage = cv2.drawKeypoint(image, keypoints, None, color, flags)
image：输入图像
keypoints：上面获取的特征点
outImage：输出图像
color：颜色，默认为随机颜色，顺序为BGR
flags：绘制点的模式，有以下四种模式
cv2.DRAW_MATCHES_FLAGS_DEFAULT：
默认值，只绘制特征点的坐标点,显示在图像上就是一个个小圆点,每个小圆点的圆心坐标都是特征点的坐标。
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS：
绘制特征点的时候绘制的是带有方向的圆,这种方法同时显示图像的坐标,size，和方向,是最能显示特征的一种绘制方式。
cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG：
只绘制特征点的坐标点,显示在图像上就是一个个小圆点,每个小圆点的圆心坐标都是特征点的坐标。
cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINT：
单点的特征点不被绘制
③ 调试
由于如下报错：
surf = cv2.xfeatures2d.SURF_create(30000)
cv2.error: OpenCV(4.2.0) C:\projects\opencv-python\opencv_contrib\modules\xfeatures2d\src\surf.cpp:1029: error: (-213:The function/feature is not implemented) This algorithm is patented and is excluded in this configuration; Set OPENCV_ENABLE_NONFREE CMake option and rebuild the library in function 'cv::xfeatures2d::SURF::create'
所以采取如下措施：
pip uninstall opencv-python
pip uninstall opencv-contrib-python
pip install opencv-python==3.4.2.16 -i "https://pypi.doubanio.com/simple/"
pip install opencv-contrib-python==3.4.2.16 -i "https://pypi.doubanio.com/simple/"
④ 代码文件
⑤ 效果图
3、错误级别分析(Error level analysis，ELA)
① 理论说明
ELA 全称：Error Level Analysis ，汉译为“错误级别分析”或者叫“误差分析”。通过检测特定压缩比率重新绘制图片后造成的误差分布，可用于识别JPEG图片的压缩。
Principe：Error Level Analysis (ELA) permits identifying areas within an image that are at different compression levels. With JPEG images, the entire picture should be at roughly the same level. If a section of the image is at a significantly different error level, then it likely indicates a digital modification.
原理：错误级别分析可以识别出一幅图片不同压缩率的地方。JPEG图像全图应该大约是相同的压缩率。如果图片的某一部分有非常突出的错误压缩率，则它可能被数字化更改过。
② 编程
为了引入 magic 文件类型识别，安装 python-magic-bin 库。
pip install -i http://mirrors.aliyun.com/pypi/simple/ python-magic-bin
代码文件：
带注释的代码
import os,sys
import magic
from PIL import Image, ImageChops, ImageEnhance
def ela(filename, output_path):
print("****ELA is starting****")
if magic.from_file(filename, mime=True) == "image/jpeg":
# set tmp_image's quality_level to be resaved
quality_level = 80
# get fileRealName,.postfix
(filerealname, extension) = os.path.splitext(os.path.basename(filename))
# set tmp_image & ela_image path
tmp_path = os.path.join(output_path,filerealname+"_tmp.jpg")
ela_path = os.path.join(output_path,filerealname+"_ela.jpg")
# resave image
image = Image.open(filename)
image.save(tmp_path, 'JPEG', quality=quality_level)
tmp_image = Image.open(tmp_path)
# return abs of difference
ela_image = ImageChops.difference(image, tmp_image)
# return (min,max) two-truples with RGB 3 elements, eg. ((0,255),(0,255),(0,255))
extrema = ela_image.getextrema()
# get the max of RGB max
max_diff = max([ex[1] for ex in extrema])
# set scale to enhance
scale = 255/max_diff
# 'Brightness' indicates we will brignten img
# 'enhance' indicates the scale of brightness
# An enhancement factor of 0.0 gives a black image. A factor of 1.0 gives the original image.
ela_image = ImageEnhance.Brightness(ela_image).enhance(scale)
ela_image.save(ela_path)
os.remove(tmp_path) # if remove this code, image will be resaved as tem_image and won't be removed.
print("****ELA has been completed****")
else:
print("ELA works only with JPEG")
if __name__ == "__main__":
filename = "./img/webOriginalImg.jpg"
output_path = "./img"
ela(filename, output_path)
③ 原图&效果图
第一组
原图
ELA高亮图
第二组
原图
网上的ELA高亮图
我自己做的ELA高亮图
第三组
图1
图2
差别
五、数据库预处理
1.提取人脸
采用 OpenCV 和 face_recognition 做对比，用15张图片做实验
代码一带注释最简版
# use OpenCV to detect face from images & save them
import cv2
import os
import time
resize_x = 256
resize_y = 256
cantFindFaceImgs = []
# Detect face rects
def detect(img, cascade, list):
rects = cascade.detectMultiScale(img, scaleFactor = 1.3, minNeighbors = 4,
flags = cv2.CASCADE_SCALE_IMAGE)
if len(rects) == 0:
print("I haven't found a face in %s"%(list))
cantFindFaceImgs.append(list)
return []
rects[:, 2:] += rects[:, :2]
return rects
if __name__ == '__main__':
start_time =time.clock()
# OpenCV Classifier
cascade = cv2.CascadeClassifier("E:\Program Files\Python\Python36\Lib\site-packages\opencv-master\data\haarcascades\haarcascade_frontalface_default.xml")
original_path = 'D:/Celeba/devel'
new_path = 'D:/Celeba_face/devel'
# os.listdir show all the filename(including extension)
imglist = os.listdir(original_path)
for list in imglist:
img = cv2.imread(original_path+'/'+list)
rects = detect(img, cascade, list)
if len(rects) == 0:
print(list)
for x1, y1, x2, y2 in rects:
face = img[y1:y2, x1:x2]
resized_face = cv2.resize(face,(resize_x, resize_y))
# Save new img, named as original name in new directory, then we can find which are not be detected
cv2.imwrite(new_path+'/CV_'+list, resized_face)
end_time = time.clock()
print("I haven't found a face in these images: %s"%(cantFindFaceImgs))
print('Running time using OpenCV is: %s Seconds'%(end_time-start_time))
代码二带注释最简版
# use face-recognition to detect face from images & save them
from PIL import Image
import face_recognition
import os
import time
resize_x = 256
resize_y = 256
cantFindFaceImgs = []
# Detect face rects
def detect(img, new_path, list):
image = face_recognition.load_image_file(img)
face_locations = face_recognition.face_locations(image)
if len(face_locations) == 0:
print("I haven't found a face in %s"%(list))
cantFindFaceImgs.append(list)
return []
for i,face_location in enumerate(face_locations):
# Get the location of each face in this image
top, right, bottom, left = face_location
face_image = image[top:bottom, left:right]
pil_image = Image.fromarray(face_image)
resized_face = pil_image.resize((resize_x, resize_y))
(filename, extension) = os.path.splitext(list)
resized_face.save(new_path+'/FR_'+filename+'_'+str(i)+extension)
if __name__ == '__main__':
start_time =time.clock()
original_path = 'D:/Celeba/devel'
new_path = 'D:/Celeba_face/devel'
# os.listdir show all the filename(including extension)
imglist = os.listdir(original_path)
for list in imglist:
img = original_path+'/'+list
detect(img, new_path, list)
end_time = time.clock()
print("I haven't found a face in these images: %s"%(cantFindFaceImgs))
print('Running time using Face-recognition is: %s Seconds'%(end_time-start_time))
输出如下：
> python findfaceCV.py
Running time using OpenCV is: 6.6083549 Seconds
> python findfaceFR.py
Running time using Face-recognition is: 9.850284 Seconds
识别截图如下：
由此可见： OpenCV 识别率低一点，时间快，脸小，矩形框范围大点儿；Face-recognition 识别率高一点，时间慢一点，脸大，矩形框范围小点儿。综上，我采用第二种方法 Face-recognition 识别。
识别数据库 Celeba devel ， Celeba test ， PGGAN ， DFD
输出如下：
# Celeba devel
I have save these images' name that I haven't found a face from in this txt: D:/Celeba_face/devel/nofound.txt
I have save face images in this path: D:/Celeba_face/devel
Not recognition rate: 0.0382536587773637
Running time using Face-recognition is: 5:09:40.564417
# Celeba test
I have save these images' name that I haven't found a face from in this txt: D:/Celeba_face/test/nofound.txt
I have save face images in this path: D:/Celeba_face/test
Not recognition rate: 0.0397808597798727
Running time using Face-recognition is: 1:35:20.080798
# PGGAN 人脸较清晰，没有进行人脸识别预处理，但是为了ELA，进行了resize和png转jpg处理
# DFD 先进行视频分帧保存图片处理，再进人脸识别步骤
2.视频分帧保存图片处理
代码
import os
import cv2
import time
import datetime
def framing():
input_path = 'D:/test'
output_path = 'D:/test_face'
txt_path = output_path+'/log.txt'
with open(txt_path, "a", encoding="utf-8") as fi:
fi.write('\n AllVideosFullName \t Index \t Frame \t Picture\n')
videos = os.listdir(input_path)
videos.sort(key = lambda x: x[:-4])
if len(videos) != 0:
video_num = 0
for each_video in videos:
print('Video {} is running ...'.format(video_num))
each_video_input = input_path+'/'+str(each_video)
each_video_output_path = output_path+'/'+str(each_video[:-4])
if not os.path.exists(each_video_output_path):
os.mkdir(each_video_output_path)
capture = cv2.VideoCapture(each_video_input)
if capture.isOpened():
real = True
else:
real = False
frame_step = 10
frame_num = 0
picture_num = 0
while real:
real, frame = capture.read()
if(frame_num % frame_step == 0):
cv2.imwrite(each_video_output_path+'/Frame'+str(frame_num)+'_Pic'+str(picture_num)+'.jpg',frame)
picture_num += 1
frame_num += 1
cv2.waitKey(1)
video_num += 1
with open(txt_path, "a", encoding="utf-8") as fi:
fi.write('{} \t {} \t {} \t {}\n'.format(each_video[:-4], video_num, frame_num, picture_num ))
capture.release()
print('Running log has been saved here: '+txt_path)
else:
print('Empty Directory')
if __name__ == '__main__':
start_time = time.clock()
framing()
end_time = time.clock()
delta_time = datetime.timedelta(seconds = (end_time-start_time))
print('Running time is: %s '%(delta_time))
测试成果：
截图
Terminal
Video 0 is running ...
Video 1 is running ...
Video 2 is running ...
Running log has been saved here: D:/test_face/log.txt
Running time is: 0:00:20.817361
log.txt
AllVideosFullName Index Frame Picture
01__exit_phone_room 1 306 31
01__hugging_happy 2 788 79
01__kitchen_pan 3 561 57
3.提取DFD视频分帧后的图片中的人脸
运行代码后，识别的人脸按原先的文件夹存放在新路径下的同名文件夹，每个文件夹都有 log.txt 记录未识别出人脸的文件以及本文件夹人脸未识别率。在新路径下有总的统计数据 log.txt ，包含： 有损图片总数， 未识别图片总数， 未受损图片总数， 总的未识别率。
分帧结果
# OUTPUT1(frame images from DFD/original_c23)
# Running log has been saved here: G:/DFD_img/original_c23/log.txt
# Running time is: 1:05:49.907241
# OUTPUT2(frame images from DFD/attack_c23)
# Running log has been saved here: G:/DFD_img/attack_c23/log.txt
# Running time is: 6:29:04.835291
找脸结果
# OUTPUT1(find face from DFD_img/original_c23)
# # of folders: 363
# Running time using Face-recognition is: 13:46:29.115011
# OUTPUT2(find face from DFD_img/attack_c23)
# # of folders: 3068
# Running time using Face-recognition is: 4 days, 4:05:53.688934
原视频人脸识别率：95.4%
生成视频人脸识别率：97.7%
4.PGGAN resize PNG->JPG
输出如下
# of file in G:/PGGAN/devel is : 6000
# of file in G:/PGGAN/test is : 3000
# of file in G:/PGGAN/train is : 21000
# of file in G:/PGGAN/img_pggan/zip000000 is : 1000
# of file in G:/PGGAN/img_pggan/zip001000 is : 1000
# of file in G:/PGGAN/img_pggan/zip002000 is : 1000
# of file in G:/PGGAN/img_pggan/zip003000 is : 1000
# of file in G:/PGGAN/img_pggan/zip004000 is : 1000
# of file in G:/PGGAN/img_pggan/zip007000 is : 1000
# of file in G:/PGGAN/img_pggan/zip008000 is : 1000
# of file in G:/PGGAN/img_pggan/zip012000 is : 1000
# of file in G:/PGGAN/img_pggan/zip013000 is : 1000
# of file in G:/PGGAN/img_pggan/zip014000 is : 1000
# of file in G:/PGGAN/img_pggan/zip016000 is : 1000
# of file in G:/PGGAN/img_pggan/zip017000 is : 1000
# of file in G:/PGGAN/img_pggan/zip018000 is : 1000
# of file in G:/PGGAN/img_pggan/zip019000 is : 1000
# of file in G:/PGGAN/img_pggan/zip025000 is : 1000
# of file in G:/PGGAN/img_pggan/zip026000 is : 1000
# of file in G:/PGGAN/img_pggan/zip028000 is : 1000
# of file in G:/PGGAN/img_pggan/zip087000 is : 1000
# of file in G:/PGGAN/img_pggan/zip088000 is : 1000
# of file in G:/PGGAN/img_pggan/zip089000 is : 1000
# of file in G:/PGGAN/img_pggan/zip090000 is : 1000
# of file in G:/PGGAN/img_pggan/zip091000 is : 1000
# of file in G:/PGGAN/img_pggan/zip092000 is : 1000
# of file in G:/PGGAN/img_pggan/zip093000 is : 1000
# of file in G:/PGGAN/img_pggan/zip094000 is : 1000
# of file in G:/PGGAN/img_pggan/zip095000 is : 1000
# of file in G:/PGGAN/img_pggan/zip096000 is : 1000
# of file in G:/PGGAN/img_pggan/zip097000 is : 1000
# of file in G:/PGGAN/img_pggan/zip098000 is : 1000
# of file in G:/PGGAN/img_pggan/zip099000 is : 1000
Running time is: 0:48:38.366194
日志如下
G:/PGGAN/devel fileslen: 6000 pngcount: 6000 notpng: 0 damaged: 0
G:/PGGAN/test fileslen: 3000 pngcount: 3000 notpng: 0 damaged: 0
G:/PGGAN/train fileslen: 21000 pngcount: 21000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip000000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip001000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip002000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip003000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip004000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip007000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip008000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip012000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip013000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip014000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip016000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip017000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip018000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip019000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip025000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip026000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip028000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip087000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip088000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip089000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip090000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip091000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip092000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip093000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip094000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip095000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip096000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip097000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip098000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
G:/PGGAN/img_pggan/zip099000 fileslen: 1000 pngcount: 1000 notpng: 0 damaged: 0
六、Celeba&PGGAN&DFD数据集特征提取
！！！注意：这里的特征提取代码只是将特征提取，然后绘制到图片上保存。后面我们将训练SVM分类器，所以需要用到特征数据，这样才比较方便。因此，后文将首先讲述特征数据的提取并保存至Excel文件，然后训练、测试SVM分类器。
matplotlib中cla() clf() close()用途
import matplotlib.pyplot as plt
plt.cla() # Clear axis即清除当前图形中的当前活动轴。其他轴不受影响。
plt.clf() # Clear figure清除所有轴，但是窗口打开，这样它可以被重复使用。
plt.close() # Close a figure window
下图只是一部分 Celeba 和 PGGAN 数据集的对比图
左上：原图，第一排PGGAN假脸，第二排Celeba真脸
右上：三原色直方图，假脸三原色峰值基本重合，真脸三原色峰值错开
左下：SURF特征点，假脸同样的阈值特征点多，真脸少
右下：ELA，假脸ELA图片发亮处较多，真脸基本一色调一致
今天把三个数据集处理(提取人脸，png转jpg)后的所有图片的3种特征都提取了一下。 ✌️
Celeba、PGGAN 特征较好，DFD 效果一般。
输出如下：
# Celeba
startTime: 2020-02-25 18:08:30.578360
endTime: 2020-02-25 22:50:47.230785
Running time: 4:42:16.652425
# PGGAN
startTime: 2020-02-25 18:09:01.274854
endTime: 2020-02-25 22:58:56.398361
Running time: 4:49:55.123507
# DFD
startTime: 2020-02-25 18:09:41.216839
endTime: 2020-02-26 12:08:03.989166
Running time: 17:58:22.772327
七、SVM分类器分类
1.练手代码
截图
2.SVM原理简介
❌ 未经许可，禁止套用！！！
3.特征数据提取
文件结构：
ExtractFeatureData # 特征数据提取代码文件夹
│ extract_feature_data.py # 特征数据提取主程序
│ OUTPUT.txt # 部分运行日志
│ test.py # 特征数据提取主程序之前的测试代码
│
└─CommonFunction # 公用函数，分别提取特征并存入excel的一个sheet
extract_color_data.py
extract_SURF_data.py
extract_ELA_data.py
三个特征分别由三个py文件提取并保存到Excel中。一组图片的同一特征存在同一个Excel文件中，每张图片占一个sheet。
① color特征：分bgr3列，每列有256*256=65536行；
② SURF特征：先提取SURF特征，核心代码如下，然后统一每张图选取半径最大的15个点作为特征点，不足则补零；
img = cv2.imread(inputpath)
surf = cv2.xfeatures2d.SURF_create(4000)
kps, features = surf.detectAndCompute(img, None)
kps_data = []
for kp in kps:
kps_data.append([kp.pt[0], kp.pt[1], kp.angle, kp.size])
③ ELA特征：首先将图片灰度化，然后提取ELA特征，每张图256行256列共65536像素。
4.SVM_SGDClassifier的训练和测试
文件结构：
SVM-SGD # SGD(Stochastic Gradient Descent)
│ svm_SGD.py # 随机梯度下降分类器主程序(含训练、测试代码)
│
└─GetData # 从excel中提取数据返回一维列表，3者基本一样
get_color.py
get_SURF.py # 3者中最先写的，注释详细
get_ELA.py
get_XXX 函数每次提取一个Excel的所有sheet的数据，返回list，每个sheet都展平为1维，占list一个元素位。
svm_SGD.py 调用三个函数获取数据，然后通过以下函数训练数据得到SVM模型、用SVM模型预测数据类别。核心代码如下：
clf = SGDClassifier()
clf.partial_fit(X, Y, classes=np.array([0, 1]))
joblib.dump(clf, savepath + '/' + 'clf.pkl')
clf2 = joblib.load(savepath+'/'+'clf.pkl')
Z = clf2.predict(X)
accuracy = clf2.score(X, Y)
OUPUT:
Running Time of 训练color特征SVM分类器 : 0:02:31.862390
测试数据实际真假：[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
测试数据预测真假：[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0]
color_clf 预测准确率：0.6164383561643836
Running Time of 测试color特征SVM分类器 : 0:02:21.229064
Running Time of 训练SURF特征SVM分类器 : 0:00:00.309207
测试数据实际真假：[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
测试数据预测真假：[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0]
SURF_clf 预测准确率：0.6438356164383562
Running Time of 测试SURF特征SVM分类器 : 0:00:00.271234
Running Time of 训练ELA特征SVM分类器 : 0:00:36.909247
测试数据实际真假：[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
测试数据预测真假：[1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 1 1 0 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0]
ELA_clf 预测准确率：0.6575342465753424
Running Time of 测试ELA特征SVM分类器 : 0:00:33.487409
最终分类器准确率大约为 63% 上下。估计很大原因是由于训练数据较少，所以准确率较低，未来工作将是大量数据训练和测试。
代码 svm_SGD_per100img.py 在训练测试大量文件时准确率不高且三者一样，感觉是代码哪里有问题，暂时还没察觉到问题所在，希望有人能看出端倪t提出建议，欢迎 New issue ！！！
八、完成论文
《开题报告》 《毕业论文》 详见：./Paper 文件夹
论文将在毕设答辩之后上传