The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Segmentation-Based Image Copy-Move Forgery Detection Scheme
Abstract
In this paper, we propose a scheme to detect the copy-move forgery in an image, mainly by extracting the keypoints for comparison. The main difference to the traditional methods is that the proposed scheme first segments the test image into semantically independent patches prior to keypoint extraction. As a result, the copy-move regions can be detected by matching between these patches. The matching process consists of two stages. In the first stage, we find the suspicious pairs of patches that may contain copy-move forgery regions, and we roughly estimate an affine transform matrix. In the second stage, an Expectation-Maximization-based algorithm is designed to refine the estimated matrix and to confirm the existence of copy-move forgery. Experimental results prove the good performance of the proposed scheme via comparing it with the state-of-the-art schemes on the public databases.
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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557
A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
An Evaluation of Popular Copy-Move Forgery Detection Approaches
Christlein, V., Riess, C., Jordan, J., Riess, C., & Angelopoulou, E. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2218597 · Cited by 802
Cited by
Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks
Zhang, K., Mao, Y., Leng, S., Zhao, Q., Li, L., Peng, X., Pan, L., Maharjan, S., & Zhang, Y. (2016) · IEEE Access · doi:10.1109/access.2016.2597169 · Cited by 855
Effective and Efficient Global Context Verification for Image Copy Detection
Zhou, Z., Wang, Y., Wu, Q. M. J., Yang, C.-N., & Sun, X. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2601065 · Cited by 336
Self-citationHossain, E., Hossain, M. F., & Rahaman, M. A. (2019) · 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) · doi:10.1109/ecace.2019.8679247 · Cited by 259
Learning from crowdsourced labeled data: a survey
Zhang, J., Wu, X., & Sheng, V. S. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9491-9 · Cited by 203
Snis: a signal noise separation-based network for post-processed image forgery detection
Chen, J., Liao, X., Wang, W., Qian, Z., Qin, Z., & Wang, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3204753 · Cited by 129
Metadata
- Authors
- Li, J., Li, X., Yang, B., & Sun, X.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2015-03
- Volume
- 10
- Issue
- 3
- Pages
- 507-518
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2052950171
- Cited by
- 810
- References
- 7
- FWCI
- 123.74
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, J., Li, X., Yang, B., & Sun, X. (2015). Segmentation-Based Image Copy-Move Forgery Detection Scheme. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2014.2381872
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