Image Copy-Move Forgery Detection Based on SIFT and Gray Level

Abstract:

In order to reduce the false matching rate when detecting copy-move forgeries, an improved method based on SIFT and gray level was proposed in this study. Firstly, extract SIFT key points, and establish SIFT feature vector for every key point; Secondly, extract the gray level feature and combine it with SIFT feature to found a feature vector with size of 129D; Finally, match the above feature vector between every two different key points and then the copy-move regions would be detected. The experimental results showed that the improved algorithm reduced false matching rate even when an image was distorted by Gaussian blur.

• Periodical: Applied Mechanics and Materials (Volumes 263-266)
• Pages: 3021-3024
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.263-266.3021
• Online since: December 2012
• Authors: Xuan Jing Shen, Ye Zhu, Ying Da Lv, Hai Peng Chen
• Keywords: Blind Identification, Copy-Move Forgery, Gray Level, SIFT Feature Vector

References

[1] Zhou Linna. Study of Digital Forensics Based on Image Content [D]. Beijing University of Posts and Telecommunications, (2007).

[2] Hany Farid. Creating and Detecting Doctored and Virtual Images: Implications to the Child Pornography Prevention Act. Department of Computer Science, (2004).

[3] Jessica Fridrich, David Soukal, Jan Lukáš. Detection of Copy-Move Forgery in Digital Images. In Proceedings of Digital Forensic Research Workshop, (2003).

[4] Alin C. Popescu, Hany Farid. Exposing Digital Forgeries by Detecting Traces of Re-sampling. IEEE Transactions on Signal Processing, Vol.53, No.2, (2005), pp.758-767.

DOI: 10.1109/tsp.2004.839932

[5] Weiqi Luo, Jiwu Huang, Guoping Qiu. Robust Detection of Region-Duplication Forgery in Digital Image. 18th International Conference on Pattern Recognition (ICPR), Vol.4, (2006), pp.746-749.

DOI: 10.1109/icpr.2006.1003

[6] Zhang Ting, Wang Rang-ding. Copy-Move Forgery Detection based on SVD in Digital Image. 2nd International Congress on Image and Signal Processing (CISP), (2009), pp.1-5.

DOI: 10.1109/cisp.2009.5301325

[7] Huang Yanping, Lu Wei, Sun Wei, Long Dongyang. Improved DCT-based Detection of Copy-move Forgery in Images. FORENSIC SCIENCE INTERNATIONAL, Vol.206, No.1-3, (2011), pp.178-184.

DOI: 10.1016/j.forsciint.2010.08.001

[8] David G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. INTERNATIONAL JOURNAL OF COMPUTER VISION, Vol.60, No.2, (2004), pp.91-110.

DOI: 10.1023/b:visi.0000029664.99615.94

[9] Yan Ke1, Rahul Sukthankar. PCA-SIFT: a More Distinctive Representation for Local Image Descriptors. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, (2004), p.II-506 - II-513.

DOI: 10.1109/cvpr.2004.1315206

[10] Herbert Bay, Tinne Tuytelaars, Luc Van Gool. SURF: Speeded Up Robust Features. 9th European Conference on Computer Vision (ECCV), Vol.3951 LNCS, (2006), pp.404-417.

DOI: 10.1007/11744023_32