Copy-move forgery detection (CMFD) is the process of determining the presence of copied areas in an image. CMFD approaches are mainly classified into two groups: keypoint-based and block-based techniques. In this paper, a new CMFD approach is proposed on the basis of both block and keypoint based approaches. Initially, the forged image is partitioned into non overlapped segments utilizing adaptive watershed segmentation, wherein adaptive H-minima transform is used for extracting the markers. Also, an Adaptive Galactic Swarm Optimization (AGSO) algorithm is used to select optimal gap parameter while selecting the markers for reducing the undesired regional minima, which can increase the segmentation performance. After that, the features from every segment are extracted as segment features (SF) using Hybrid Wavelet Hadamard Transform (HWHT). Then, feature matching is performed using adaptive thresholding. The false matches or outliers can be removed with the help of Random Sample Consensus (RANSAC) algorithm. Finally, the Forgery Region Extraction Algorithm (FREA) is utilized for detecting the copied portion from the host image. Experimental results indicate that the proposed scheme find out image forgery region with Precision = 92.45%; Recall = 93.67% and F1 = 92.75% on MICC-F600 dataset and Precision = 94.52%; Recall = 95.32% and F1 = 93.56% on Bench mark dataset at pixel level. Also, it outperforms the existing approaches when the image undergone certain geometrical transformation and image degradation.

复制移动伪造检测（CMFD）是确定图像中是否存在复制区域的过程。CMFD方法主要分为两类：基于关键点的技术和基于块的技术。本文在基于块和关键点的方法的基础上提出了一种新的CMFD方法。最初，利用自适应分水岭分割将伪造图像划分为非重叠段，其中，将自适应H-最小变换用于提取标记。此外，在选择标记以减少不希望的区域最小值时，使用自适应银河群优化算法（AGSO）选择最佳间隙参数，这可以提高分割性能。之后，使用混合小波Hadamard变换（HWHT）将每个分段的特征提取为分段特征（SF）。然后，使用自适应阈值执行特征匹配。错误匹配或离群值可以借助随机样本共识（RANSAC）算法消除。最后，伪造区域提取算法（FREA）用于从主机图像中检测复制的部分。实验结果表明，该方案能找到图像伪造区域，其精度为92.45％。在MICC-F600数据集上，召回率= 93.67％，F1 = 92.75％，而Precision = 94.52％; 在像素级别的基准数据集上，召回率= 95.32％，F1 = 93.56％。同样，当图像经历某些几何变换和图像退化时，它的性能也优于现有方法。伪造区域提取算法（FREA）用于检测宿主图像中的复制部分。实验结果表明，该方案能找到图像伪造区域，其精度为92.45％。在MICC-F600数据集上，召回率= 93.67％，F1 = 92.75％，而Precision = 94.52％; 在像素级别的基准数据集上，召回率= 95.32％，F1 = 93.56％。而且，当图像经历某些几何变换和图像退化时，它的性能优于现有方法。伪造区域提取算法（FREA）用于检测宿主图像中的复制部分。实验结果表明，该方案能找到图像伪造区域，其精度为92.45％。在MICC-F600数据集上，召回率= 93.67％，F1 = 92.75％，而Precision = 94.52％; 在像素级别的基准数据集上，召回率= 95.32％，F1 = 93.56％。而且，当图像经历某些几何变换和图像退化时，它的性能优于现有方法。