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<p>Copy-move forgery, where an image region is copied into another part of the same image, is one of the most common and easy-to-implement image tampering techniques. Keypoint (also called feature point)-based detection methods exhibit remarkable performance in terms of computational cost and robustness. However, these methods have the following limitations to varying degrees: 1) Failure to extract keypoints from small or smooth regions; 2) Lack of robust and discriminative descriptors for keypoints; 3) Low accuracy and excessive computational cost of keypoints matching; 4) High false negative/positive rate caused by the defects of post processing. To overcome such limitations, we propose an adaptive copy move forgery detection based on new keypoint feature and matching in this paper. Firstly, based on the simple linear iterative clustering (SLIC) and the multi-directional multi-layer double-cross pattern (MDML-DCP), i.e., the segmentation method is adopted, and uniform key points are adaptively extracted from the whole image (including small and smooth regions) by fitting the MDML-DCP value-threshold function of superpixels. Then, due to the strong robustness of moment features to attacks and their stronger descriptive ability compared to traditional invariant moments. Secondly, texture features can get more distinctive features. Next, accurate quaternion fractional pseudo-Jacobi-Fourier moments (AQFPJFM) and gradient local ternary patterns (GLTP) describe the key points to obtain robust and discriminative features. Then, the ITQ-PTH algorithm is introduced for keypoint matching, which is more accurate than traditional locality-sensitive hashing algorithms and can improve the matching accuracy. Finally, the false negative rate and false positive rate are reduced by reliable post-processing methods. Experimental results show that the proposed method achieves an F-score of 96.61% and 95.18% on GRIP and MICC-F600 datasets, respectively, which is 2.71 and 3.07 percentage points higher than the latest method, and the accuracy is also higher.</p>

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Adaptive copy move forgery detection based on new keypoint feature and matching

  • 期刊论文

  • 发表日期：

    2025年7月12日

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  • Xiangyang Wang,
  • Huiying Zhang,
  • Dawei Wang,
  • Panpan Niu

摘要

Copy-move forgery, where an image region is copied into another part of the same image, is one of the most common and easy-to-implement image tampering techniques. Keypoint (also called feature point)-based detection methods exhibit remarkable performance in terms of computational cost and robustness. However, these methods have the following limitations to varying degrees: 1) Failure to extract keypoints from small or smooth regions; 2) Lack of robust and discriminative descriptors for keypoints; 3) Low accuracy and excessive computational cost of keypoints matching; 4) High false negative/positive rate caused by the defects of post processing. To overcome such limitations, we propose an adaptive copy move forgery detection based on new keypoint feature and matching in this paper. Firstly, based on the simple linear iterative clustering (SLIC) and the multi-directional multi-layer double-cross pattern (MDML-DCP), i.e., the segmentation method is adopted, and uniform key points are adaptively extracted from the whole image (including small and smooth regions) by fitting the MDML-DCP value-threshold function of superpixels. Then, due to the strong robustness of moment features to attacks and their stronger descriptive ability compared to traditional invariant moments. Secondly, texture features can get more distinctive features. Next, accurate quaternion fractional pseudo-Jacobi-Fourier moments (AQFPJFM) and gradient local ternary patterns (GLTP) describe the key points to obtain robust and discriminative features. Then, the ITQ-PTH algorithm is introduced for keypoint matching, which is more accurate than traditional locality-sensitive hashing algorithms and can improve the matching accuracy. Finally, the false negative rate and false positive rate are reduced by reliable post-processing methods. Experimental results show that the proposed method achieves an F-score of 96.61% and 95.18% on GRIP and MICC-F600 datasets, respectively, which is 2.71 and 3.07 percentage points higher than the latest method, and the accuracy is also higher.