Journal of Engineering and Applied Sciences

Year: 2018
Volume: 13
Issue: 8 SI
Page No. 6371 - 6377

Website Forgery/Falsification Detection System Using Link Information and Images

Authors : Ji-Ho Cho and Geuk Lee

References

Anonymous, 2014. OpenCV documentation ORB. OpenCV, Massachusetts, USA. http://docs.opencv.org/3.0-beta/.

Bank of Korea, 2016. Present situation of use of domestic internet banking services in the 3rd quarter of 2016. NTT DATA, Tokyo, Japan. http://www.nttdata.com/global/en/investor/library/results-briefing/pdf/2016/fy2015_pre_3q_01.pdf.

Calonder, M., V. Lepetit, C. Strecha and P. Fua, 2010. Brief: Binary Robust Independent Elementary Features. In: Computer Vision-ECCV, Daniilidis, K., P. Maragos and N. Paragios (Eds.). Springer, Berlin, Germany, ISBN:978-3-642-15560-4, pp: 778-792.

Ji-Yong, S., C. Ji-Ho, H. Lee, K. Jeong-Min and G. Lee, 2016. A study on website forgery/falsification detection techniques using images. J. Convergence Secur., 16: 81-87.

Rosten, E. and T. Drummond, 2006. Machine learning for high-speed corner detection. Eur. Conf. Comput. Vision, 1: 430-443.
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Rublee, E., V. Rabaud, K. Konolige and B. Gary, 2011. ORB: An efficient alternative to SIFT or SURF. Proceedings of the IEEE International Conference on Computer Vision (ICCV), November 6-13, 2011, IEEE, Barcelona, Spain, ISBN:978-1-4577-1101-5, pp: 2564-2571.

Yu, L., Z. Yu and Y. Gong, 2015. An improved ORB algorithm of extracting and matching features. Intl. J. Signal Process. Image Process. Pattern Recognit., 8: 117-126.
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