Abstract
Image splicing is a forgery technique where some regions are cropped or pasted from the same or different images. Splicing localization becomes challenging when post-processing techniques are used to remove the anomalies of splicing traces. In this chapter, an improved method is proposed for blurred image splicing localization based on K-nearest neighbor (KNN) matting. The proposed method minimizes computation time without compromising the quality of the result. Quantitative and qualitative results analysis show the proposed method obtains better splicing than existing systems.
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
Chen, J., L. Yuan, C.-K. Tang and L. Quan. 2008. Robust dual motion deblurring. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), 18.
Liu, R., Z. Li and J. Jia. 2008. Image partial blur detection and classification, In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), 18.
Su, B., S. Lu, and C. L. Tan. 2011. Blurred image region detection and classification. In Proceedings of the 19th ACM international conference on multimedia, 1397–1400.
Bahrami, Khosro, Alex C. Kot, Leida Li and Haoliang Li. 2015. Blurred image splicing localization by exposing blur type inconsistency. In IEEE Transactions on Information Forensics and Security, 5 (5): 999–1009.
Sharifi, K., and A. Leon-Garcia. 1995. Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video. IEEE Transactions on Circuits and Systems for Video Technology 5 (1): 52–56.
Hu, Zhe, and Ming-Hsuan Yang. 2012. Good Regions to Deblur. In European conference on computer vision, 59–72.
Levin, A., Y. Weiss, F. Durand, and W.T. Freeman. 2011. Efficient marginal likelihood optimization in blind deconvolution. In 2011 Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), 2657–2664.
McLachlan, G.J. 2004. Discriminant analysis and statistical pattern recognition. Hoboken, NJ, USA: Wiley.
Chen, Qifeng, Dingzeyu Li, and Chi-Keung Tang. 2013. KNN Matting. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (9): 2175–2188.
Levin, A., D. Lischinski, and Y. Weiss. 2008. A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (2): 228–242.
Bahrami K., and A.C. Kot. 2014. Image tampering detection by exposing blur type inconsistency. In Proceedings of IEEE ICASSP, May 2014, 2654–2658.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Abhijith, P.S., Simon, P. (2019). Improved Blurred Image Splicing Localization with KNN Matting. In: Krishna, A., Srikantaiah, K., Naveena, C. (eds) Integrated Intelligent Computing, Communication and Security. Studies in Computational Intelligence, vol 771. Springer, Singapore. https://doi.org/10.1007/978-981-10-8797-4_62
Download citation
DOI: https://doi.org/10.1007/978-981-10-8797-4_62
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8796-7
Online ISBN: 978-981-10-8797-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)