如何使用OpenCV特征匹配技术检测复制-粘贴篡改？

Question

如何使用OpenCV特征匹配技术检测复制-粘贴篡改？

c++opencvsiftflann

8

在我的opencv项目中，我想要检测图像中的复制移动伪造。我知道如何使用opencv FLANN进行2个不同图像的特征匹配，但是我对如何在图像中使用FLANN进行复制移动伪造检测感到困惑。

附注1：我获取了图像的SIFT关键点和描述符，并卡在使用特征匹配类上。

附注2：特征匹配的类型对我来说不重要。

提前致谢。

更新：

这些图片是我需要的示例。

以下是一段匹配两张图像特征并在两张图像上执行类似操作的代码（不是单张图像），这段代码是以Android原生OpenCV格式编写的：

    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);

- Mohamad MohamadPoor

1

展示您当前的代码和正在使用的图像样本肯定会很有帮助。 - alexisrozhkov

@user3896254，感谢您的建议，我已经编辑了我的帖子并添加了示例和代码。 - Mohamad MohamadPoor

1

各位，你们知道如何使用Python在图像中实现字体(字符/数字)的复制和移动伪造吗？ - Azam Rafique

@MohamadMohamadPoor，你有没有找到满意的答案解决这个问题？ - jtlz2

1个回答

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- Gwen · Accepted Answer

“我不确定使用关键点是否是解决这个问题的好方法。我更愿意尝试模板匹配（使用滑动窗口在图像上作为补丁）。与关键点相比，这种方法的缺点是对旋转和比例敏感。

如果您想使用关键点，可以：”

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);