Image Copy–Move Forgery Detection Using Combination of Scale-Invariant Feature Transform and Local Binary Pattern Features
Abstract
Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people’s lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer’s mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy–move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75%, 95.45%, and 87% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75% to 3.75% on the GRIP dataset, has been able to achieve the best results.
References
- 1. , “A fast SIFT based method for copy move forgery detection,” Future Comput. Inform. J. 3(2), 159–165 (2018). https://doi.org/10.1016/j.fcij.2018.03.001. Crossref, Google Scholar
- 2. , “Copy-move forgery detection: Survey, challenges and future directions,” J. Netw. Comput. Appl. 75, 259–278 (2016). https://doi.org/10.1016/j.jnca.2016.09.008. Crossref, Web of Science, Google Scholar
- 3. , “An efficient and robust method for detecting copy-move forgery,” in Proc. 2009 IEEE Int. Conf. Acoustics, Speech and Signal Processing (2009), pp. 1053–1056. https://doi.org/10.1109/icassp.2009.4959768. Crossref, Google Scholar
- 4. , “Copy-move forgery detection and localization by means of robust clustering with J-Linkage,” Signal Process., Image Commun. 28(6), 659–669 (2013). https://doi.org/10.1016/j.image.2013.03.006. Crossref, Web of Science, Google Scholar
- 5. , “Detection of copy-move forgery in digital images using SIFT algorithm,” in Proc. 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Vol. 2 (2008), pp. 272–276. https://doi.org/10.1109/PACIIA.2008.240. Crossref, Google Scholar
- 6. , “A survey on Image Forgery Detection techniques,” in Proc. 2015 IEEE RIVF Int. Conf. Computing & Communication Technologies — Research, Innovation, and Vision for Future (RIVF) (2015), pp. 71–76. https://doi.org/10.1109/RIVF.2015.7049877. Crossref, Google Scholar
- 7. , “Nonoverlapping blocks based copy-move forgery detection,” Secur. Commun. Netw. 2018, 1301290 (2018). https://doi.org/10.1155/2018/1301290. Crossref, Web of Science, Google Scholar
- 8. , “Fast and effective image copy-move forgery detection via hierarchical feature point matching,” IEEE Trans. Inf. Forensics Sec. 14(5), 1307–1322 (2019). https://doi.org/10.1109/TIFS.2018.2876837. Crossref, Web of Science, Google Scholar
- 9. , “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002). https://doi.org/10.1109/tpami.2002.1017623. Crossref, Web of Science, Google Scholar
- 10. , “An evaluation of popular copy-move forgery detection approaches,” IEEE Trans. Inf. Forensics Sec. 7(6), 1841–1854 (2012). https://doi.org/10.1109/tifs.2012.2218597. Crossref, Web of Science, Google Scholar
- 11. , “Copy move forgery detection based on keypoint and patch match,” Multimedia Tools Appl. 78(22), 31387–31413 (2019). https://doi.org/10.1007/s11042-019-07930-5. Crossref, Web of Science, Google Scholar
- 12. , “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60(2), 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94. Crossref, Web of Science, Google Scholar
- 13. , “A comparison of SIFT, PCA-SIFT and SURF,” Int. J. Image Process. 3(4), 143–152 (2009). Google Scholar
- 14. , “Image forgery detection using deep learning: A survey,” in Proc. 6th Int. Conf. Advanced Computing and Communication Systems (ICACCS) (2020), pp. 571–576. https://doi.org/10.1109/ICACCS48705.2020.9074408. Crossref, Google Scholar
- 15. , “A deep learning approach to detection of splicing and copy-move forgeries in images,” in Proc. 2016 IEEE Int. Workshop on Information Forensics and Security (WIFS) (2016), pp. 1–6. https://doi.org/10.1109/WIFS.2016.7823911. Crossref, Google Scholar
- 16. , “An efficient method for image forgery detection based on trigonometric transforms and deep learning,” Multimedia Tools Appl. 79(25), 18221–18243 (2020). https://doi.org/10.1007/s11042-019-08162-3. Crossref, Web of Science, Google Scholar
- 17. , “Determination of the optimal threshold value and number of keypoints in scale invariant feature transform-based copy-move forgery detection,” Int. J. Electron. Telecommun. 66(3), 561–569 (2020). Crossref, Web of Science, Google Scholar
- 18. , “Efficient dense-field copy–move forgery detection,” IEEE Trans. Inf. Forensics Sec. 10(11), 2284–2297 (2015). https://doi.org/10.1109/TIFS.2015.2455334. Crossref, Web of Science, Google Scholar
- 19. , “Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes,” J. Vis. Commun. Image Represent. 29, 16–32 (2015). https://doi.org/10.1016/j.jvcir.2015.01.016. Crossref, Web of Science, Google Scholar
- 20. , “Iterative copy-move forgery detection based on a new interest point detector,” IEEE Trans. Inf. Forensics Sec. 11(11), 2499–2512 (2016). https://doi.org/10.1109/TIFS.2016.2585118. Crossref, Web of Science, Google Scholar
- 21. , “Copy-move forgery detection using scale invariant feature and reduced local binary pattern histogram,” Symmetry 12(4), 492 (2020). Crossref, Web of Science, Google Scholar
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