Gaussian Pyramid Decomposition in Copy-Move Image Forgery Detection with SIFT and Zernike Moment Algorithms

Firstyani Imannisa Rahma, Ema Utami, Hanif Al-Fatta


One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.


Copy-Move Forgery;Scale Invariant Feature Transform;Gaussian Pyramid Decomposition;Zernike Moments

Full Text:

PDF (Indonesian)


Al-Qershi, O. M., & Khoo, B. E. (2018). Evaluation of Copy-move Forgery Detection: Datasets and Evaluation Metrics. Multimedia Tools and Applications, 77(24), 31807–31833. doi: 10.1007/s11042-018-6201-4

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. doi: 10.1109/TIFS.2011.2129512

Christlein, V., Riess, C., Jordan, J., Riess, C., & Angelopoulou, E. (2012). An Evaluation of Popular Copy-move Forgery Detection Approaches. IEEE Transactions on Information Forensics and Security, 7(6), 1841–1854. doi: 10.1109/TIFS.2012.2218597

Hailing, H., Weiqiang, G., & Yu, Z. (2008). Detection of Copy-move Forgery in Digital Images Using SIFT Algorithm. Proceedings - 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008, 2, 272–276. doi: 10.1109/PACIIA.2008.240

Liang, Z., Yang, G., Ding, X., & Li, L. (2015). An Efficient Forgery Detection Algorithm for Object Removal by Exemplar-based Image Inpainting. Journal of Visual Communication and Image Representation, 30, 75–85. Elsevier Inc. doi: 10.1007/s11042-017-4829-0

Lowe, D. G. (1999). Object Recognition from Local Scale-invariant Features. Proceedings of the Seventh IEEE International Conference on Computer Vision (Vol. 482, pp. 1150–1157 vol.2). IEEE. doi: 10.1109/ICCV.1999.790410

Lowe, D. G. (2004). Distinctive Image Features from Scale-invariant Keypoints. International Journal of Computer Vision, 60(2), 91–110. doi: 10.1023/B:VISI.0000029664.99615.94

Mohamadian, Z., & Pouyan, A. A. (2013). Detection of Duplication Forgery in Digital Images in Uniform and Non-uniform Regions. Proceedings - UKSim 15th International Conference on Computer Modelling and Simulation, UKSim 2013, 1, 455–460. doi: 10.1109/UKSim.2013.94

Pun, C. M., Yuan, X. C., & Bi, X. L. (2015). Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching. IEEE Transactions on Information Forensics and Security, 10(8), 1705–1716. doi: 10.1109/TIFS.2015.2423261

Rahma, F. I., Utami, E., & Fatta, H. Al. (2020). The Using of Gaussian Pyramid Decomposition, Compact Watershed Segmentation Masking and DBSCAN in Copy-Move Forgery Detection with SIFT. 2020 3rd International Conference on Information and Communications Technology (ICOIACT) (pp. 325–330). IEEE. doi: 10.1109/ICOIACT50329.2020.9332081

Riadi, I., Fadlil, A., & Sari, T. (2017). Image Forensic for Detecting Splicing Image with Distance Function. International Journal of Computer Applications, 169(5), 6–10. doi: 10.5120/ijca2017914729

Ryu, S.-J., Lee, M.-J., & Lee, H.-K. (2010). Detection of Copy-Rotate-Move Forgery Using Zernike Moments. International Workshop on Information Hiding (pp. 51–65). doi: 10.1007/978-3-642-16435-4_5

Sadeghi, S., Dadkhah, S., Jalab, H. A., Mazzola, G., & Uliyan, D. (2018). State of The Art in Passive Digital Image Forgery Detection: Copy-move Image Forgery. Pattern Analysis and Applications, 21(2), 291–306. Springer London. doi: 10.1007/s10044-017-0678-8

Shabanian, H., & Mashhadi, F. (2018). A New Approach for Detecting Copy-move Forgery in Digital Images. 2017 IEEE Western New York Image and Signal Processing Workshop, WNYISPW 2017, (November), 1–6. doi: 10.1109/ICCS.2008.4737205

Soni, B., Das, P. K., & Thounaojam, D. M. (2018). MultiCMFD: Fast and Efficient System for Multiple Copy-move Forgeries Detection in Image. ACM International Conference Proceeding Series (pp. 53–58). doi: 10.1145/3191442.3191465

Sun, Y., Ni, R., & Zhao, Y. (2018). Nonoverlapping Blocks Based Copy-Move Forgery Detection. Security and Communication Networks, 2018. doi: 10.1155/2018/1301290

Tan, W., Wu, Y., Wu, P., & Chen, B. (2019). A Survey on Digital Image Copy-Move Forgery Localization Using Passive Techniques. Journal of New Media, 1(1), 11–25. doi: 10.32604/jnm.2019.06219

Teague, M. R. (1980). Image Analysis Via The General Theory of Moments. Journal of the Optical Society of America, 70(8), 920. Retrieved from

Tyagi, V. (2018). Understanding Digital Image Processing. Boca Raton: CRC Press. doi: 10.1201/9781315123905

Walia, S., & Kumar, K. (2019). Digital Image Forgery Detection: a Systematic Scrutiny. Australian Journal of Forensic Sciences, 51(5), 488–526. doi: 10.1080/00450618.2018.1424241

Warif, N. B. A., Wahab, A. W. A., Idris, M. Y. I., Salleh, R., & Othman, F. (2017). SIFT-Symmetry: A Robust Detection Method for Copy-move Forgery with Reflection Attack. Journal of Visual Communication and Image Representation, 46, 219–232. doi: 10.1016/j.jvcir.2017.04.004

Zheng, J., Liu, Y., Ren, J., Zhu, T., Yan, Y., & Yang, H. (2016). Fusion of block and keypoints based approaches for effective copy-move image forgery detection. Multidimensional Systems and Signal Processing, 27(4), 989–1005. doi: 10.1007/s11045-016-0416-1



  • There are currently no refbacks.


Indexed by:       


ISSN 2442-4528 (online) | ISSN 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto Telp (0281) 623321 Fax (0281) 621662


Creative Commons License
This work is licensed under a  Creative Commons Attribution 4.0 International License.