Exposing Digital Image Forgeries by Illumination Color Classification

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-18 Number-6
Year of Publication : 2014
Authors : Ms. Shraddha R Asati , Mr. P.R.Pardhi
  10.14445/22315381/IJETT-V18P255

MLA 

Ms. Shraddha R Asati , Mr. P.R.Pardhi "Exposing Digital Image Forgeries by Illumination Color Classification", International Journal of Engineering Trends and Technology (IJETT), V18(6),269-271 Dec 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

For years, photographs have been used to document space-time events and they have often served as evidence in courts. Photographers are able to create composites of analog pictures, but this process is very time consuming and requires expert knowledge. Today, however, advanced digital image editing software makes image alterations straightforward. These challenges our trust in photographs and, in particular, questions pictures as evidence for real-world events. In this paper, we examine one of the most common forms of photographic operation, well-known as image composition or splicing. In this work, we make an significant step towards reducing user interaction for an illuminant-based tampering decision making. We offer a new semiautomatic method that is more reliable than previous approaches. In this method we propose the method to detect the forensic in the photography. For that here we use the SVM classifier for the forensic detection. Initially we identify the illuminant map in the image. We find the face from the photography. For the face detect here we use the violo john method. After face detection we identify the GLCM (Gray Level Co-Occurance Matrix). In GLCM is the statistical information of the image such as energy, entropy, correlation sum of energy and sum of correlation are calculated. And also we extract the LBP feature. The extracted feature will pass to the SVM classifier for the training. SVM is stands for Support vector machine. It is a binary classifier. It is a kernel based learning classifier. The trained classifier will predict about the image whether it is original or forensic image.

References

[1] Tiago Jose de Carvalho,Christian Riess,Elli Angelopoulou and Helio Pedrini "Exposing Digital Image Forgeries By Illumination Color Classification" IEEE Trans. Inf. Forensics Security ,Vol. 8, no. 7, pp. 1182 - 1194, July 2013.
[2] R. Kawakami, K. Ikeuchi, and R. T. Tan, “Consistent surface color for texturing large objects in outdoor scenes,” in Proc. IEEE Int. Conf. Comput. Vision, 2005, pp. 1200–1207.
[3] S. Gholap and P. K. Bora, “Illuminant colour based image forensics,” in Proc. IEEE Region 10 Conf., 2008, pp. 1–5.
[4] J. F. O’Brien and H. Farid, “Exposing photo manipulation with inconsistent reflections,” ACM Trans. Graphics, vol. 31, no. 1, pp. 1–11, Jan. 2012.
[5] J. Lukas, J. Fridrich, andM.Goljan, “Digital camera identification from sensor pattern noise,” IEEE Trans. Inf. Forensics Security, vol. 1, no.2, pp. 205–214, Jun. 2006.
[6] M. Johnson and H. Farid, “Exposing digital forgeries by detecting inconsistencies in lighting,” in Proc. ACM Workshop on Multimedia and Security, New York, NY, USA, 2005, pp. 1–10.
[7] Y. Ostrovsky, P. Cavanagh, and P. Sinha, “Perceiving illumination inconsistencies in scenes,” Perception, vol. 34, no. 11, pp. 1301–1314, 2005.
[8] S. Bianco and R. Schettini, “Color constancy using faces,” in Proc. IEEE Comput. Vision and Pattern Recognition, Providence, RI, USA, Jun. 2012.
[9] W. Fan, K. Wang, F. Cayre, and Z. Xiong, “3D lighting-based image forgery detection using shape-from-shading,” in Proc. Eur. Signal Processing Conf. (EUSIPCO), Aug. 2012, pp. 1777–1781.
[10] Farid H,”Image forgery detection – a survey”, IEEE Signal Processing Magazine March 2009; 5:16–25
[11] R. Tan, K. Nishino, and K. Ikeuchi, “Color constancy through inverse- intensity chromaticity space,” J. Opt. Soc. Amer. A, vol. 21, pp. 321–334, 2004.
[12] J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process., vol. 16, no. 9, pp. 2207–2214, Sep. 2007.
[13] H. R. Chennamma, Lalitha Rangarajan “Image Splicing Detection Using Inherent Lens Radial Distortion” IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 6, November 2010.pp 149-158.