如何使用 opencv 特征匹配来检测复制移动伪造

Question

在我的 opencv 项目中，我想检测图像中的复制移动伪造。我知道如何使用 opencv FLANN 在 2 个不同的图像中进行特征匹配，但我对如何使用 FLANN 检测图像中的复制移动伪造感到非常困惑。

P.S1：我得到了图像的筛选关键点和描述符，并坚持使用特征匹配类。

P.S2：特征匹配的类型对我来说并不重要。

提前致谢。

更新：

这些图片是我需要的一个例子

并且有一个代码匹配两个图像的特征并在两个图像（不是一个）上做类似的事情，android原生opencv格式的代码如下：

    vector<KeyPoint> keypoints;
        Mat descriptors;

        // Create a SIFT keypoint detector.
        SiftFeatureDetector detector;
        detector.detect(image_gray, keypoints);
        LOGI("Detected %d Keypoints ...", (int) keypoints.size());

        // Compute feature description.
        detector.compute(image, keypoints, descriptors);
        LOGI("Compute Feature ...");


        FlannBasedMatcher matcher;
        std::vector< DMatch > matches;
        matcher.match( descriptors, descriptors, matches );

        double max_dist = 0; double min_dist = 100;

        //-- Quick calculation of max and min distances between keypoints
          for( int i = 0; i < descriptors.rows; i++ )
          { double dist = matches[i].distance;
            if( dist < min_dist ) min_dist = dist;
            if( dist > max_dist ) max_dist = dist;
          }

          printf("-- Max dist : %f \n", max_dist );
          printf("-- Min dist : %f \n", min_dist );

          //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
          //-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
          //-- small)
          //-- PS.- radiusMatch can also be used here.
          std::vector< DMatch > good_matches;

          for( int i = 0; i < descriptors.rows; i++ )
          { if( matches[i].distance <= max(2*min_dist, 0.02) )
            { good_matches.push_back( matches[i]); }
          }

          //-- Draw only "good" matches
          Mat img_matches;
          drawMatches( image, keypoints, image, keypoints,
                       good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
                       vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

          //-- Show detected matches
//          imshow( "Good Matches", img_matches );
          imwrite(imgOutFile, img_matches);

Answer 1

我不知道对这个问题使用关键点是否是个好主意。我宁愿测试template matching（使用图像上的滑动窗口作为补丁）。与关键点相比，这种方法的缺点是对旋转和缩放敏感。

如果你想使用关键点，你可以：

• 找到一组关键点（SURF、SIFT 或任何您想要的），
• 使用蛮力匹配器 (cv::BFMatcher) 的 knnMatch 函数计算与所有其他关键点的匹配分数，

• 保持不同点之间的匹配，即距离大于零（或阈值）的点。

  int nknn = 10; // max number of matches for each keypoint
  double minDist = 0.5; // distance threshold
  
  // Match each keypoint with every other keypoints
  cv::BFMatcher matcher(cv::NORM_L2, false);
  std::vector< std::vector< cv::DMatch > > matches;
  matcher.knnMatch(descriptors, descriptors, matches, nknn);
  
  double max_dist = 0; double min_dist = 100;
  
  //-- Quick calculation of max and min distances between keypoints
  for( int i = 0; i < descriptors.rows; i++ )
  { 
      double dist = matches[i].distance;
      if( dist < min_dist ) min_dist = dist;
      if( dist > max_dist ) max_dist = dist;
  }
  
  // Compute distance and store distant matches
  std::vector< cv::DMatch > good_matches;
  for (int i = 0; i < matches.size(); i++)
  {
      for (int j = 0; j < matches[i].size(); j++)
      {
          // The METRIC distance
          if( matches[i][j].distance> max(2*min_dist, 0.02) )
              continue;
  
          // The PIXELIC distance
          Point2f pt1 = keypoints[queryIdx].pt;
          Point2f pt2 = keypoints[trainIdx].pt;
  
          double dist = cv::norm(pt1 - pt2);
          if (dist > minDist)
              good_matches.push_back(matches[i][j]);
      }
  }
  
  Mat img_matches;
  drawMatches(image_gray, keypoints, image_gray, keypoints, good_matches, img_matches);