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Currently, image copy–move forgery issues have brought increasingly serious problems to the social fields. Many research have been devoted to address the image copy–move forgery issues. However, image copy–move forgery detection (CMFD) is still a challenging problem to image forensics. This paper proposes a coarse-to-fine detection method fusing the superiorities of both keypoint-based and block-based methods. The fusion method gets good geometric invariances of keypoint-based methods and good matching robustness with the invariant moment of block-based methods. In the coarse detection stage, a robust SIFT descriptor is used to extract the candidate keypoints, and then the 2NN test is applied to match the suspicious keypoint couples. The proposed three-pass filtering relying on the Euclidean distance, scaling coefficient ratio, and correlation coefficient of block-based features, remove the false-positive outlier couples. Finally, the scaling coefficient ratio statistics of the remaining keypoint couples get the scaling coefficient ratio between the size of copied/pasted or pasted/copied snippets. In the fine detection stage, the block-based thought relying on the scaling coefficient ratio uses the DAFMT to extract the block-based features of multiple scaling levels. Subsequently, LSH is presented to classify block features and finally indicate the fine forgery snippets. Finally, the morphological operations are presented to indicate the forgery accurately. The benchmark IMD and CoMoFoD image copy–move datasets are used to measure the performances between the proposed fusion method and the state-of-the-art CMFD methods. The numerous experimental results demonstrate that the proposed fusion method achieves nearly the best performances to resist various attacks, especially large-scaling attacks, among the compared state-of-the-art methods. [ABSTRACT FROM AUTHOR]