KAZE Feature Based Passive Image Forgery Detection
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Copy-move image forgery is a most common tampering artifact. It can be carried out by copy-pasting a region of the same image; thus, it has become a challenging one to find. So, this paper put forwarding a method to detect such forgery by extracting the KAZE features. RANSAC algorithm is functioned to get rid off the false matches such as outliers, and then the forged image is disclosed. The experiment is carried out using the publically available datasets, and their performances are quantitatively assessed using the true positive rate and false positive rate. A comparative analysis is also done with state-of-the-art methods, and it is certified that the proposed method produced good results than the other methods.
KeywordsCopy-move forgery KAZE RANSAC Clustering TPR FPR
The authors express their gratitude and credits for the use of the MICC-F220 and MICC-F2000 databases.
- 1.A. J. Fridrich, B. D. Soukal, and A. J. Luk, “Detection of copy-move forgery in digital images,” in in Proceedings of Digital Forensic Research Workshop, 2003.Google Scholar
- 5.M. Puri and V. Chopra, “A survey: Copy-Move forgery detection methods,” International journal of computer systems, vol. 3, 2016.Google Scholar
- 8.I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, L. Del Tongo, and G. Serra, “Copy-move forgery detection and localization by means of robust clustering with J-linkage,” Signal Processing: Image Communication, vol. 28, no. 6, pp. 659–669, 2013.Google Scholar
- 10.P. Mishra, N. Mishra, S. Sharma, and R. Patel, “Region Duplication Forgery Detection Technique Based on SURF and HAC,” The Scientific World Journal, vol. 2013, 2013.Google Scholar
- 11.Y. Zhu, X. Shen, and H. Chen, “Copy-move forgery detection based on scaled ORB,” Multimedia Tools and Applications, pp. 1–13, 2015.Google Scholar
- 13.P. L. Jiming ZHENG, “Detection of Copy-move Forgery in Digital Image using DAISY Descriptor,” Journal of Computational Information Systems, vol. 10, pp. 9369–9377, 2014.Google Scholar
- 14.V. Anand, M. F. Hashmi, and A. G. Keskar, “A copy move forgery detection to overcome sustained attacks using dyadic wavelet transform and sift methods,” in Intelligent Information and Database Systems, Springer, 2014, pp. 530–542.Google Scholar
- 15.M. F. Hashmi, A. R. Hambarde, and A. G. Keskar, “Copy move forgery detection using DWT and SIFT features,” in Intelligent Systems Design and Applications (ISDA), 2013 13th International Conference on, 2013, pp. 188–193.Google Scholar
- 17.A. S. Alfraih, J. A. Briffa, and S. Wesemeyer, “Cloning localization based on feature extraction and k-means clustering,” in Digital-Forensics and Watermarking, Springer, 2014, pp. 410–419.Google Scholar