How to use opencv feature matching for detecting copy-move forgery

Question

In my opencv project, I want to detect copy-move forgery in an image. I know how to use the opencv FLANN for feature matching in 2 different image, but I am become so confused on how to use FLANN for detection copy-move forgery in an image.

P.S1: I get the sift keypoints and descriptors of image and stuck in using the feature matching class.

P.S2: the type of feature matching is not important for me.

Thanks in advance.

Update :

These pictures is an example of what I need

And There is a code which matches features of two images and do something like it on two images (not a single one), the code in android native opencv format is like below:

    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

I don't know if it's a good idea to use keypoints for this problem. I'd rather test template matching (using a sliding window on your image as patch). Compared to keypoints, this method has the disadvantage of being sensible to rotation and scale.

If you want to use keypoints, you can :

• find a set of keypoints (SURF, SIFT, or whatever you want),
• compute the matching score with every other keypoints, with the knnMatch function of the Brute Force Matcher (cv::BFMatcher),

• keep matches between distincts points, i.e. points whose distance is greater than zero (or a threshold).

  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);