Modified SIFT-Based Kirsch Edge Detection Approach for Copy-Move Forgery Detection
DOI:
https://doi.org/10.35877/454RI.asci3939Keywords:
Copy-move forgery detection, image forensics, digital image forgery, Kirsch edge detector, SIFT descriptor.Abstract
The increasing accessibility of digital imaging technology has led to a rise in image forgery, raising concerns about digital content authenticity in forensic and security domains. Copy-move forgery is one the most prevalent and challenging forgery techniques due to its seamless manipulation. We propose a novel passive CMFD (CMFD) approach that leverages a modified Kirsch (mKirsch) edge detector and a modified SIFT-based descriptor (DivSIFT) to accurately identify and localize copy-move forgery (CMF). The mKirsch edge detector enhances edge detection by selectively deleting specific masks, improving keypoint extraction and feature matching. We used MICC-F220, CoMoFoD, and MICC-F8Multi datasets to measure the performance of the new method, under challenging conditions such as rotation, scaling, JPEG compression, and multiple forgeries. The results show that mKirsch-enhanced detection outperforms compared to conventional Kirsch-based methods. Notably, methods with deleted masks (WW_NW and NE_SE) achieved a True Positive Rate (TPR) of 90.91%, precision 100%, and an F-measure of 95.24%. Robust against rotation and scaling attacks, achieving a TPR of up to 96.97% with zero false positives. Additionally, the method is computationally efficient, with an execution time of 2.74 seconds, making it suitable for real-world applications. These findings establish the mKirsch-based CMFD as a highly accurate and efficient solution for image forgery detection in digital images.
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References
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Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011). A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security, 6(3 PART 2), 1099–1110. https://doi.org/10.1109/TIFS.2011.2129512
Arandjelovic, R., & Zisserman, A. (2012). Three things everyone should know to improve object retrieval. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2911–2918. https://doi.org/10.1109/CVPR.2012.6248018
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Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
Bellavia, F., & Colombo, C. (2020). RootsGloh2: Embedding RootSIFT “square rooting” in sGLOH2. IET Computer Vision, 14(4), 138–143. https://doi.org/10.1049/iet-cvi.2019.0716
Bursuc, A., Tolias, G., & Jégou, H. (2015). Kernel local descriptors with implicit rotation matching. ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval, 595–598. https://doi.org/10.1145/2671188.2749379
Chen, Chenglong, Ni, J., Shen, Z., & Shi, Y. Q. (2017). Blind Forensics of Successive Geometric Transformations in Digital Images Using Spectral Method: Theory and Applications. IEEE Transactions on Image Processing, 26(6), 2811–2824. https://doi.org/10.1109/TIP.2017.2682963
Chen, Chien-chang, Lu, W., & Chou, C. (2019). Rotational copy-move forgery detection using SIFT and region growing strategies. 151.
Chou, C. L., & Lee, J. C. (2018). Copy-move forgery detection based on local gabor wavelets patterns. Advances in Intelligent Systems and Computing, 733, 47–56. https://doi.org/10.1007/978-3-319-76451-1_5
Cozzolino, D., Poggi, G., & Verdoliva, L. (2014). COPY-MOVE FORGERY DETECTION BASED ON PATCHMATCH Davide Cozzolino , Giovanni Poggi , Luisa Verdoliva Universit ´ a Federico II di Napoli , DIETI , 80125 Naples Italy. International Conference on Image Processing(ICIP), 5312–5316.
Dixit, R., & Naskar, R. (2017). Review, analysis and parameterisation of techniques for copy-move forgery detection in digital images. IET Image Processing, 11(9), 746–759. https://doi.org/10.1049/iet-ipr.2016.0322
Fu, G., Zhang, Y., & Wang, Y. (2023). Image copy-move forgery detection based on fused features and density clustering. Applied Sciences, 13(13), 7528.
Fridrich, J., Soukal, D., & Lukáš, J. (2003). Detection of Copy-Move Forgery in Digital Images. Proceedings of Digital Forensic Research Workshop, Cleveland, OH, 1163–1168. https://doi.org/10.1109/ICMLA.2015.137
Gani, G., & Qadir, F. (2020). A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata. Journal of Information Security and Applications, 54, 102510. https://doi.org/10.1016/j.jisa.2020.102510
Genton, M. G. (2001). Classes of kernels for machine learning: a statistics perspective. Journal of machine learning research, 2(Dec), 299-312.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.
Hegazi, A., Taha, A., & Selim, M. M. (2021). An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal. Journal of King Saud University - Computer and Information Sciences, 33(9), 1055–1063. https://doi.org/10.1016/j.jksuci.2019.07.007
Hosny, K. M., Hamza, H. M., & Lashin, N. A. (2019). Copy-for-duplication forgery detection in colour images using QPCETMs and sub-image approach. IET Image Processing, 13(9), 1437–1446. https://doi.org/10.1049/iet-ipr.2018.5356
Jiang, L., Lu, Z., Gao, Y., & Wang, Y. (2024). Image Copy-Move Forgery Detection and Localization Scheme: How to Avoid Missed Detection and False Alarm. arXiv preprint arXiv:2406.03271.
Joglekar, N. P., & Chatur, P. N. (2015). A Compressive Survey on Active and Passive Methods for Image Forgery Detection. International Journal Of Engineering And Computer Science, 4(1), 10187–10190.
Korus, P. (2017). Digital image integrity – a survey of protection and verification techniques. Digital Signal Processing: A Review Journal, 71, 1–26. https://doi.org/10.1016/j.dsp.2017.08.009
Liu, K., Lu, W., Lin, C., Huang, X., Liu, X., Yeung, Y., & Xue, Y. (2019). Copy move forgery detection based on keypoint and patch match. Multimedia Tools and Applications, 78(22), 31387–31413. https://doi.org/10.1007/s11042-019-07930-5
Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. 60(2), 91–110.
Mikolajczyk, K. (2002). Detection of local features invariant to affine transformations Application to matching and recognition. Most, 171.
Niyishaka, P., & Bhagvati, C. (2020). Copy-move forgery detection using image blobs and BRISK feature. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-020-09225-6
Pan, X., & Lyu, S. (2010). Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security, 5(4), 857–867. https://doi.org/10.1109/TIFS.2010.2078506
Pardo, L. (2018). Statistical inference based on divergence measures. Chapman and Hall/CRC.
Pavlovi?, A., Glišovi?, N., Gavrovska, A., & Reljin, I. (2019). Copy-move forgery detection based on multifractals. Multimedia Tools and Applications, 78(15), 20655–20678. https://doi.org/10.1007/s11042-019-7277-1
Prakash, C. S., Panzade, P. P., Om, H., & Maheshkar, S. (2019). Detection of copy-move forgery using AKAZE and SIFT keypoint extraction. Multimedia Tools and Applications, 78(16), 23535–23558. https://doi.org/10.1007/s11042-019-7629-x
Rao, Y., & Ni, J. (2017). A deep learning approach to detection of splicing and copy-move forgeries in images. 8th IEEE International Workshop on Information Forensics and Security, WIFS 2016, 1–6. https://doi.org/10.1109/WIFS.2016.7823911
Soni, B., Das, P. K., & Thounaojam, D. M. (2019). Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features. Journal of Information Security and Applications, 45, 44–51. https://doi.org/10.1016/j.jisa.2019.01.007
Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge university press.
Teerakanok, S., & Uehara, T. (2019). Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis. IEEE Access, 7, 40550–40568. https://doi.org/10.1109/ACCESS.2019.2907316
Wang, J., Liu, G., Li, H., Dai, Y., & Wang, Z. (2009). Detection of image region duplication forgery using model with circle block. 1st International Conference on Multimedia Information Networking and Security, MINES 2009, 1, 25–29. https://doi.org/10.1109/MINES.2009.142
Wang, Z., Fan, B., Wang, G., & Wu, F. (2016). Exploring Local and Overall Ordinal Information for Robust Feature Description. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2198–2211. https://doi.org/10.1109/TPAMI.2015.2513396
Warif, N. B. A., Wahab, A. W. A., Idris, M. Y. I., Ramli, R., Salleh, R., Shamshirband, S., & Choo, K. K. R. (2016). Copy-move forgery detection: Survey, challenges and future directions. Journal of Network and Computer Applications, 75, 259–278. https://doi.org/10.1016/j.jnca.2016.09.008
Yang, B., Sun, X., & Guo, H. (2018). A copy-move forgery detection method based on CMFD-SIFT. Multimedia Tools and Applications, 1800, 837–855. https://doi.org/10.1007/s11042-016-4289-y
Zheng, L., Zhang, Y., & Thing, V. L. L. (2019). A survey on image tampering and its detection in real-world photos. Journal of Visual Communication and Image Representation, 58(December), 380–399. https://doi.org/10.1016/j.jvcir.2018.12.022
Zhou, Z., Wang, Y., Wu, Q. M. J., Yang, C. N., & Sun, X. (2017). Effective and Efficient Global Context Verification for Image Copy Detection. IEEE Transactions on Information Forensics and Security, 12(1), 48–63. https://doi.org/10.1109/TIFS.2016.2601065
Zhu, Y., Shen, X., & Chen, H. (2015). Copy-move forgery detection based on scaled ORB. 2699. https://doi.org/10.1007/s11042-014-2431-2
Alhaidery, M. M. A., Taherinia, A. H., & Shahadi, H. I. (2023). A robust detection and localization technique for copy-move forgery in digital images. Journal of King Saud University-Computer and Information Sciences, 35(1), 449-461.
Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011). A SIFT-based forensic method for copy-move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security, 6(3 PART 2), 1099–1110. https://doi.org/10.1109/TIFS.2011.2129512
Arandjelovic, R., & Zisserman, A. (2012). Three things everyone should know to improve object retrieval. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2911–2918. https://doi.org/10.1109/CVPR.2012.6248018
Asghar, K., Habib, Z., & Hussain, M. (2016). Copy-move and splicing image forgery detection and localization techniques?: a review. Australian Journal of Forensic Sciences, 0618(May), 0–27. https://doi.org/10.1080/00450618.2016.1153711
Bayram. (2009). AN EFFICIENT AND ROBUST METHOD FOR DETECTING COPY-MOVE FORGERY Sevinc Bayram Polytechnic Institute of NYU ECE Dept . Brooklyn , NY Husrev Taha Sencar TOBB University of Economics & Technology Ankara , TURKEY Nasir Memon Polytechnic Insitute of NYU CIS Dep. Image (Rochester, N.Y.), 1053–1056.
Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
Bellavia, F., & Colombo, C. (2020). RootsGloh2: Embedding RootSIFT “square rooting” in sGLOH2. IET Computer Vision, 14(4), 138–143. https://doi.org/10.1049/iet-cvi.2019.0716
Bursuc, A., Tolias, G., & Jégou, H. (2015). Kernel local descriptors with implicit rotation matching. ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval, 595–598. https://doi.org/10.1145/2671188.2749379
Chen, Chenglong, Ni, J., Shen, Z., & Shi, Y. Q. (2017). Blind Forensics of Successive Geometric Transformations in Digital Images Using Spectral Method: Theory and Applications. IEEE Transactions on Image Processing, 26(6), 2811–2824. https://doi.org/10.1109/TIP.2017.2682963
Chen, Chien-chang, Lu, W., & Chou, C. (2019). Rotational copy-move forgery detection using SIFT and region growing strategies. 151.
Chou, C. L., & Lee, J. C. (2018). Copy-move forgery detection based on local gabor wavelets patterns. Advances in Intelligent Systems and Computing, 733, 47–56. https://doi.org/10.1007/978-3-319-76451-1_5
Cozzolino, D., Poggi, G., & Verdoliva, L. (2014). COPY-MOVE FORGERY DETECTION BASED ON PATCHMATCH Davide Cozzolino , Giovanni Poggi , Luisa Verdoliva Universit ´ a Federico II di Napoli , DIETI , 80125 Naples Italy. International Conference on Image Processing(ICIP), 5312–5316.
Dixit, R., & Naskar, R. (2017). Review, analysis and parameterisation of techniques for copy-move forgery detection in digital images. IET Image Processing, 11(9), 746–759. https://doi.org/10.1049/iet-ipr.2016.0322
Fu, G., Zhang, Y., & Wang, Y. (2023). Image copy-move forgery detection based on fused features and density clustering. Applied Sciences, 13(13), 7528.
Fridrich, J., Soukal, D., & Lukáš, J. (2003). Detection of Copy-Move Forgery in Digital Images. Proceedings of Digital Forensic Research Workshop, Cleveland, OH, 1163–1168. https://doi.org/10.1109/ICMLA.2015.137
Gani, G., & Qadir, F. (2020). A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata. Journal of Information Security and Applications, 54, 102510. https://doi.org/10.1016/j.jisa.2020.102510
Genton, M. G. (2001). Classes of kernels for machine learning: a statistics perspective. Journal of machine learning research, 2(Dec), 299-312.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.
Hegazi, A., Taha, A., & Selim, M. M. (2021). An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal. Journal of King Saud University - Computer and Information Sciences, 33(9), 1055–1063. https://doi.org/10.1016/j.jksuci.2019.07.007
Hosny, K. M., Hamza, H. M., & Lashin, N. A. (2019). Copy-for-duplication forgery detection in colour images using QPCETMs and sub-image approach. IET Image Processing, 13(9), 1437–1446. https://doi.org/10.1049/iet-ipr.2018.5356
Jiang, L., Lu, Z., Gao, Y., & Wang, Y. (2024). Image Copy-Move Forgery Detection and Localization Scheme: How to Avoid Missed Detection and False Alarm. arXiv preprint arXiv:2406.03271.
Joglekar, N. P., & Chatur, P. N. (2015). A Compressive Survey on Active and Passive Methods for Image Forgery Detection. International Journal Of Engineering And Computer Science, 4(1), 10187–10190.
Korus, P. (2017). Digital image integrity – a survey of protection and verification techniques. Digital Signal Processing: A Review Journal, 71, 1–26. https://doi.org/10.1016/j.dsp.2017.08.009
Liu, K., Lu, W., Lin, C., Huang, X., Liu, X., Yeung, Y., & Xue, Y. (2019). Copy move forgery detection based on keypoint and patch match. Multimedia Tools and Applications, 78(22), 31387–31413. https://doi.org/10.1007/s11042-019-07930-5
Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. 60(2), 91–110.
Mikolajczyk, K. (2002). Detection of local features invariant to affine transformations Application to matching and recognition. Most, 171.
Niyishaka, P., & Bhagvati, C. (2020). Copy-move forgery detection using image blobs and BRISK feature. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-020-09225-6
Pan, X., & Lyu, S. (2010). Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security, 5(4), 857–867. https://doi.org/10.1109/TIFS.2010.2078506
Pardo, L. (2018). Statistical inference based on divergence measures. Chapman and Hall/CRC.
Pavlovi?, A., Glišovi?, N., Gavrovska, A., & Reljin, I. (2019). Copy-move forgery detection based on multifractals. Multimedia Tools and Applications, 78(15), 20655–20678. https://doi.org/10.1007/s11042-019-7277-1
Prakash, C. S., Panzade, P. P., Om, H., & Maheshkar, S. (2019). Detection of copy-move forgery using AKAZE and SIFT keypoint extraction. Multimedia Tools and Applications, 78(16), 23535–23558. https://doi.org/10.1007/s11042-019-7629-x
Rao, Y., & Ni, J. (2017). A deep learning approach to detection of splicing and copy-move forgeries in images. 8th IEEE International Workshop on Information Forensics and Security, WIFS 2016, 1–6. https://doi.org/10.1109/WIFS.2016.7823911
Soni, B., Das, P. K., & Thounaojam, D. M. (2019). Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features. Journal of Information Security and Applications, 45, 44–51. https://doi.org/10.1016/j.jisa.2019.01.007
Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge university press.
Teerakanok, S., & Uehara, T. (2019). Copy-Move Forgery Detection: A State-of-the-Art Technical Review and Analysis. IEEE Access, 7, 40550–40568. https://doi.org/10.1109/ACCESS.2019.2907316
Wang, J., Liu, G., Li, H., Dai, Y., & Wang, Z. (2009). Detection of image region duplication forgery using model with circle block. 1st International Conference on Multimedia Information Networking and Security, MINES 2009, 1, 25–29. https://doi.org/10.1109/MINES.2009.142
Wang, Z., Fan, B., Wang, G., & Wu, F. (2016). Exploring Local and Overall Ordinal Information for Robust Feature Description. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2198–2211. https://doi.org/10.1109/TPAMI.2015.2513396
Warif, N. B. A., Wahab, A. W. A., Idris, M. Y. I., Ramli, R., Salleh, R., Shamshirband, S., & Choo, K. K. R. (2016). Copy-move forgery detection: Survey, challenges and future directions. Journal of Network and Computer Applications, 75, 259–278. https://doi.org/10.1016/j.jnca.2016.09.008
Yang, B., Sun, X., & Guo, H. (2018). A copy-move forgery detection method based on CMFD-SIFT. Multimedia Tools and Applications, 1800, 837–855. https://doi.org/10.1007/s11042-016-4289-y
Zheng, L., Zhang, Y., & Thing, V. L. L. (2019). A survey on image tampering and its detection in real-world photos. Journal of Visual Communication and Image Representation, 58(December), 380–399. https://doi.org/10.1016/j.jvcir.2018.12.022
Zhou, Z., Wang, Y., Wu, Q. M. J., Yang, C. N., & Sun, X. (2017). Effective and Efficient Global Context Verification for Image Copy Detection. IEEE Transactions on Information Forensics and Security, 12(1), 48–63. https://doi.org/10.1109/TIFS.2016.2601065
Zhu, Y., Shen, X., & Chen, H. (2015). Copy-move forgery detection based on scaled ORB. 2699. https://doi.org/10.1007/s11042-014-2431-2
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Published
2025-08-31
How to Cite
Idris, B., Abdullah, L. N., Halin, A. A., & Selimun, M. T. A. (2025). Modified SIFT-Based Kirsch Edge Detection Approach for Copy-Move Forgery Detection. Journal of Applied Science, Engineering, Technology, and Education, 7(2), 195–209. https://doi.org/10.35877/454RI.asci3939
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