ABSTRACT Copy-move forgery is one of the most popular tampering artefacts in digital images. However, tampering effect in digital images makes the authentication of the processing as untrustworthy. In this paper, a combination of Fourier-Mellin and Zernike moments (FMZM) Transform is proposed which detects the copy-move region with high-speed and low-computational complexity. Here, initially an image is segmented into various blocks using marker controlled watershed management and from that proposed FMZM feature extraction is used which detects duplication. The detected regions are matched with the Dense Depth Reconstruction based lexicographically sorting. Finally, tampered outliers presented at the data are removed through RANSAC (RANdom Sample Consensus) algorithm, in which removed false matches are verified with the morphological operators. The efficiency of proposed method is measured by various performance metrics and this method earned up to 97.56%, 99.98%, and 97.12% for precision, recall, and F1-score performance, respectively.

中文翻译：

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基于密集深度块匹配的复制移动数字图像伪造检测的鲁棒法医变换

摘要 复制移动伪造是数字图像中最流行的篡改人工制品之一。然而，数字图像中的篡改效应使得处理的认证不可信。在本文中，提出了傅里叶-梅林和泽尼克矩（FMZM）变换的组合，以高速和低计算复杂度检测复制移动区域。在这里，最初使用标记控制的分水岭管理将图像分割成各种块，然后使用建议的 FMZM 特征提取来检测重复。检测到的区域与基于 Dense Depth Reconstruction 的字典排序匹配。最后，通过RANSAC（RANdom Sample Consensus）算法去除数据中出现的篡改异常值，其中使用形态算子验证去除的错误匹配。