Abstract
With the rapid development of image editing software, forged images have become a serious social problem because of their great destructiveness. Copy-move is one of the most commonly used types of forgery. The keypoint-based copy-move forgery detection (CMFD) techniques identify forged regions by extracting image keypoints and using local visual features. These methods show remarkable detection capability in some areas, such as memory requirements and computational cost. After years of research, the challenges of the current keypoint-based methods are as follows: 1) The number or distribution of extracted keypoints is not satisfactory, especially in smoothed and textured areas. 2) Many local visual features have poor robustness to geometric attacks and post-processing disturbances, which fundamentally limits the performance of algorithms. 3) The existing post-processing algorithms can’t effectively filter out false matched pairs and accurately locate the tampered areas. To overcome these problems, an accurate and robust image copy-move forgery detection method using adaptive keypoints and hybrid features is proposed. Firstly, an adaptive keypoint extraction method based on the simple linear iterative clustering (SLIC) and the K-multiple-means (KMM) is proposed, which can extract the dense and uniform keypoints in the whole image. Then, we combine the transform domain features based on the Fast Quaternion Generic Polar Complex Exponential Transform (FQGPCET) and the texture features based on the Gray-level co-occurrence matrix (GLCM) to obtain the robust hybrid features. The hybrid features have outstanding descriptive power. Then the feature matching is performed by the double-bit quantized locally sensitive hash (DBQ-LSH). Finally, a high-precision post-processing algorithm includes two-step filtering and two-step clustering is proposed. The experimental results demonstrate that the overall performance of the proposed algorithm is superior to that of other solutions for detecting copy-move forgery images.
Similar content being viewed by others
Data availability
Data available on request from the authors.
References
Abdel-Basset M, Manogaran G, Fakhry AE et al (2020) 2-levels of clustering strategy to detect and locate copy-move forgery in digital images. Multimed Tools Appl 79(7):5419–5437
Achanta R, Shaji A, Smith K et al (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282
Amerini I, Ballan L, Caldelli R et al (2013) Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process Image Commun 28(6):659–669
Anbu T, Joe MM, Murugeswari G (2021) A comprehensive survey of detecting tampered images and localization of the tampered region. Multimed Tools Appl 80:2713–2751
Ardizzone E, Bruno A, Mazzola G (2015) Copy-move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensic Secur 10(10):2084–2094
Barni M, Phan QT, Tondi B (2021) Copy move source-target disambiguation through multi-branch CNNs. IEEE Trans Inf Forensic Secur 16:1825–1840
Chang IC, Yu JC, Chang CC (2013) A forgery detection algorithm for exemplar-based inpainting images using multi-region relation. Image Vis Comput 31(1):57–71
Christlein V, Riess C, Jordan J et al (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensic Secur 7(6):1841–1854
Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy-move forgery detection. IEEE Trans Inf Forensic Secur 10(11):2284–2297
Emam M, Han Q, Niu X (2016) PCET based copy-move forgery detection in images under geometric transforms. Multimed Tools Appl 75(18):11513–11527
Fadl SM, Semary NA (2017) Robust copy-move forgery revealing in digital images using polar coordinate system. Neurocomputing 265:57–65
Fridrich AJ, Soukal BD, Lukas AJ (2013) Detection of copy-move forgery in digital images, in: Proceedings of digital forensic research workshop, Cleveland, Ohio, USA, p. 55–61
Gan Y, Zhong J, Vong C (2022) A novel copy-move forgery detection algorithm via feature label matching and hierarchical segmentation filtering. Inf Process Manag 59(1):102783
Gani G, Qadir F (2020) A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata. J Inf Secur Appl 54:102510
Gani G, Qadir F (2021) Copy move forgery detection using DCT, PatchMatch and cellular automata. Multimed Tools Appl 80(21):32219–32243
Garg M, Dhiman G (2020) A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Comput & Applic 33:1311–1328
Hoang TV, Tabbone S (2011) Generic polar harmonic transforms for invariant image description, in: 2011 18th IEEE international conference on image processing (ICIP), Brussels, Belgium, 829–832
Huang H, Guo W, Zhang Y (2008) Detection of copy-move forgery in digital images using SIFT algorithm, in: 2008 IEEE Pacific-Asia workshop on computational intelligence and industrial application, Wuhan, China, 272–276
Johansson H (2014) Sampling and quantization. Acad Press Lib Signal Process 1:169–244
Jwaid MF, Baraskar TN (2017) Detection of copy-move image forgery using local binary pattern with discrete wavelet transform and principle component analysis, in: 2017 international conference on computing, Communication. Control and Automation (ICCUBEA), Pune, India, pp 1–6
Kalsi DK, Rai P (2017) A copy-move forgery detection system using approximation image local binary pattern, in: 2017 international conference on recent innovations in signal processing and embedded systems (RISE), Bhopal, India, 284–288
Kim KS, Zhang D, Kang MC, et al. (2013) Improved simple linear iterative clustering superpixels, in: 2013 IEEE international symposium on consumer electronics (ISCE), Hsinchu, Taiwan, 259–260
Kong W, Li WJ (2012) Double-bit quantization for hashing, in: Proceedings of the AAAI conference on artificial intelligence (AAAI), Toronto, Ontario, Canada, 634–640
Li Y, Zhou J (2018) Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE Trans Inf Forensic Secur 14(5):1307–1322
Li J, Li X, Yang B et al (2014) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensic Secur 10(3):507–518
Liu Y, Guan Q, Zhao X (2018) Copy-move forgery detection based on convolutional kernel network. Multimed Tools Appl 77(14):18269–18293
Lyu Q, Luo J, Liu K et al (2021) Copy move forgery detection based on double matching. J Vis Commun Image Represent 76:103057
Mahmood T, Mehmood Z, Shah M et al (2018) A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform. J Vis Commun Image Represent 53:202–214
Nie F, Wang LC, Li X (2019) K-multiple-means: a multiple-means clustering method with specified k clusters, in: Proceedings of the 25th ACM SIGKDD international conference on Knowledge Discovery & Data Mining (KDD), Anchorage, Alaska, USA, 959–967
Niu PP, Wang C, Chen WC et al (2021) Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants. J Vis Commun Image Represent 77:103068
Priyanka SG, Singh K (2020) An improved block based copy-move forgery detection technique. Multimed Tools Appl 79(19):13011–13035
Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensic Secur 10(8):1705–1716
Ryu SJ, Kirchner M, Lee MJ et al (2013) Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensic Secur 8(8):1355–1370
Sadeghi S, Dadkhah S, Jalab HA et al (2018) State of the art in passive digital image forgery detection: copy-move image forgery. Pattern Anal Applic 21(2):291–306
Soni B, Das PK, Thounaojam DM (2018) CMFD: a detailed review of block based and key feature based techniques in image copy-move forgery detection. IET Image Process 12(2):167–178
Soni B, Das PK, Thounaojam DM (2018) Keypoints based enhanced multiple copy-move forgeries detection system using density-based spatial clustering of application with noise clustering algorithm. IET Image Process 12(11):2092–2099
Tahaoglu G, Ulutas G, Ustubioglu B et al (2021) Improved copy move forgery detection method via L*a*b* color space and enhanced localization technique. Multimed Tools Appl 80(15):23419–23456
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Wang Y, Tian L, Li C (2017) LBP-SVD based copy move forgery detection algorithm, in: 2017 IEEE international symposium on multimedia (ISM), Taichung, Taiwan, 553–556
Wang XY, Jiao LX, Wang XB et al (2018) A new keypoint-based copy-move forgery detection for color image. Appl Intell 48(10):3630–3652
Wang X, Liu Y, Xu H et al (2018) Robust copy-move forgery detection using quaternion exponent moments. Pattern Anal Applic 21(2):451–467
Wang Y, Kang X, Chen Y (2020) Robust and accurate detection of image copy-move forgery using PCET-SVD and histogram of block similarity measures. J Inf Secur Appl 54:102536
Wang XY, Wang C, Wang L et al (2020) A fast and high accurate image copy-move forgery detection approach. Multidim Syst Sign Process 31(3):857–883
Wang XY, Wang C, Wang L et al (2021) Robust and effective multiple copy-move forgeries detection and localization. Pattern Anal Applic 24(3):1025–1046
Wu Y, Almageed WA, Natarajan P (2018) Image copy-move forgery detection via an end-to-end deep neural network, in: 2018 IEEE winter conference on applications of computer vision (WACV), Lake Tahoe, NV, USA, 1907–1915
Yang HY, Qi SR, Niu PP, Wang XY (2020) Color image zero-watermarking based on fast quaternion generic polar complex exponential transform. Signal Process Image Commun 82:115747
Yang J, Liang Z, Gan Y et al (2021) A novel copy-move forgery detection algorithm via two-stage filtering. Digit Signal Process 113:103032
Zhong JL, Pun CM (2019) An end-to-end dense-inceptionnet for image copy-move forgery detection. IEEE Trans Inf Forensic Secur 15:2134–2146
Zhong JL, Pun CM (2020) Two-pass hashing feature representation and searching method for copy-move forgery detection. Inf Sci 512:675–692
Acknowledgments
This work was supported partially by the National Natural Science Foundation of China (Nos. 61472171 & 61701212), Key Scientific Research Project of Liaoning Provincial Education Department (No. LJKZZ20220115), and Scientific Research Project of Liaoning Provincial Education Department (No. LJKMZ20221420).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
Ethical standard
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, Xy., Wang, Xq., Niu, Pp. et al. Accurate and robust image copy-move forgery detection using adaptive keypoints and FQGPCET-GLCM feature. Multimed Tools Appl 83, 2203–2235 (2024). https://doi.org/10.1007/s11042-023-15499-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-15499-3