Abstract
Digital images can be edited with the help of photo editing tools to improve or enhance the image quality. On the other hand, digital images can also be subject to manipulations which can alter the visual information being conveyed by the image. The forged images can also be used to spread false information through various media platforms and in some cases may be surreptitiously used as false evidence in a court of law. Therefore, it is crucial to test the authenticity of such images and ensure that it does not spread falsified information. One of the most common types of forgery being used today is copy-move forgery in which one part of the image is copied and placed over another part of the same image in order to either conceal certain details or multiply certain features seen in the original image. This work introduces a method of detecting copy-move forgery in digital images using speeded-up robust features (SURF) to extract keypoints from the image and then uses k-nearest neighbor (kNN) training and mapping to yield accurate matches. The SURF algorithm is capable of performing equally or even exceed the more widely accepted SIFT-based counterparts in terms of ensuring distinctive features, reproducibility, and robustness. As a result, this technique ensures a robust detection of copy move forgery while ensuring lower computational costs compared to the SIFT-based techniques used for the same purpose.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bay, H., Ess, A., Tutyelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008)
Christlein, V., Riess, C., Jordan, J., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)
Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A SIFT-based forensic method for copy move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)
Fridrich, A.J., Soukal, B.D., Lukáš, A.J.: Detection of copy-move forgery in digital images. In: Digital Forensic Research Workshop, DFRWS. Citeseer (2003)
Wang, J., Liu, G., Li, H., Dai, Y., Wang, Z.: Detection of image region duplication forgery using model with circle block. In: International Conference on Multimedia Information Networking and Security, Hubei, China. MINES, vol. 1, pp. 25–29. IEEE (2009)
Bayram, S., Sencar, H.T., Memon, N.: An efficient and robust method for detecting copy-move forgery. In: International Conference on Acoustics, Speech and Signal Processing, ICASSP, Taipei, Taiwan, pp. 1053–1056. IEEE (2009)
Ryu, S.-J., Lee, M.-J., Lee, H.-K.: Detection of copy-rotate-move forgery using Zernike moments. In: Information Hiding, pp. 51–65. Springer, Heidelberg (2010)
Cozzolino, D., Poggi, G., Verdoliva, L.: Copy-move forgery detection based on patch match. In: International Conference on Image Processing, ICIP, Paris, France, pp. 5312–5316. IEEE (2014)
Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using SIFT algorithm. In: Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Wuhan, China. PACIIA, vol. 2, pp. 272–276. IEEE (2008)
Pan, X., Lyu, S.: Region duplication detection using image feature matching. IEEE Trans. Inf. Forensics Secur. 5(4), 857–867 (2010)
Silva, E., Carvalho, T., Ferreira, A., Rocha, A.: 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)
Li, J., Li, X., Yang, B., Sun, X.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Jin, G., Wan, X.: An Improved method for SIFT-based copy-move forgery detection using non-maximum value suppression and optimized J-Linkage. Signal Process. Image Commun. 57, 113–125 (2017)
Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Del Tongo, L., Serra, G.: Copy-move forgery detection and localization by means of robust clustering with J-Linkage. Signal Process. Image Commun. 28(6), 659–669 (2013)
Drews Jr., P., de Bem, R., de Melo, A.: Analyzing and exploring feature detectors in images (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Paul, K.H., Akshatha, K.R., Karunakar, A.K., Seshadri, S. (2020). SURF Based Copy Move Forgery Detection Using kNN Mapping. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-17798-0_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17797-3
Online ISBN: 978-3-030-17798-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)