Colour-Range Histogram technique for Automatic Image Source Detection

Nancy Chinyere Woods, Charles Abiodun Robert


Computer generated images are visually becoming increasingly genuine, due to advances in technology as well as good graphic applications.  Consequently, making distinction between computer generated images and natural images is no longer a simple task.  Manual identification of computer generated images have failed to resolve the problems associated with legal issues on exact qualification of images. In this work, a colour range histogram was developed to categorise colours in computer generated images and natural images from a point of reference. Four groups were selected, using the algorithm, consisting of exact Red-Green-Blue (RGB) code (group 1), colour code within a range of 10 (group 2), colour code within a range of 20 (group 3) and colour code within a range of 30 (group 4) from the point of reference.  An optimised equation for the four Colour Code Groups (CCG) was developed.  The computer generated images categorised an average of 69.8%, 92.9%, 96.9% and 98.6%, of any colour code for groups 1, 2, 3 and 4, respectively. The categorised colours for natural images were 31.1%, 82.6%, 90.8% and 95.0% for groups 1, 2, 3 and 4, respectively. The results showed that natural images contain a wide range of RGB colours which makes them different.  Consequently, the disparity in the percentage of colours categorised can be used to differentiate computer generated images from natural images.

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Oracle, "The Java tutorials," 2 Febuary 2012.

[Online]. Available: 7-tutorial-2012-02-28-1536013.html. [Accessed 14 June 2013].

M. K. Johnson, K. Dale, S. Avidan, H. Pfister, W. T. Freeman and W. Matusik, (2011) "CG2Real: Improving the Realism of Computer Generated Images using a Large Collection of Photographs," IEEE Transactions on Visualization and Computer Graphics, vol. XVII, no. 9, pp. 1273 - 1285.

T.-t. Ng, S.-f. Chang, C.-y. Lin and Q. Sun, (2006) "Passive-blind Image Forensics," in In Multimedia Security Technologies for Digital Rights, Elsevier, pp. 383- 412.

G. K. Birajdar and V. H. Mankar, (2013). "Digital image forgery detection using passive techniques: A survey," Digital Investigation, vol. 10, no. 3, pp. 226- 245.

G. K. Birajdar and V. H. Mankar, (2017). "Computer Graphic and Photographic Image Classification using Local Image Descriptors," Defence Science Journal, vol. 67, no. 6, pp. 654-663.

F. Peng, J. Liu and M. Long, (2012). "Identification of Natural Images and Computer Generated Graphics Based on Hybrid Features," International Journal of Digital Crime and Forensics, vol. IV, no. 1, pp. 1-16.

A. Swaminathan, M. Wu and K. J. R. Liu, (2006). "Component forensics of digital cameras: A nonintrusive approach," in 2006 40th Annual Conference on Information Sciences and Systems.

M. Chandra, S. Pandey and R. Chaudhary, (2010). "Digital watermarking technique for protecting digital images," in 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT).

A. C. Popescu and H. Farid, (2005). "Exposing digital forgeries in color filter array interpolated images," IEEE Transactions on Signal Processing, vol. 53, pp. 3948-3959.

Wang W., Dong J., Tan T. (2009) A Survey of Passive Image Tampering Detection. In: Ho A.T.S., Shi Y.Q., Kim H.J., Barni M. (eds) Digital Watermarking. IWDW 2009. Lecture Notes in Computer Science, vol 5703. Springer, Berlin, Heidelberg

S. D. Mahalakshmia, K. Vijayalakshmib and S. Priyadharsinia, (2012). "Digital image forgery detection and estimation by exploring basic image manipulations," Digital Investigation, pp. 215-225.

S. Lyu and H. Farid, (2005). "How realistic is photorealistic?," IEEE Transactions on Signal Processing, vol. 53, no. 2, pp. 845-850.

J. Lukas, J. Fridrich and M. Goljan, (2005). "Determining digital image origin using sensor imperfections," in SPIE Electronic Imaging, Image and Video Communication and Processing, San Jose, California.

H. Farid and S. Lyu, (2003). "Higher-order wavelet statistics and their application to digital forensics," in Computer Vision and Pattern Recognition.

T.-T. Ng, S.-F. Chang, J. Hsu, L. Xie and M.-P. Tsui, (2005). "Physics-motivated features for distinguishing photographic images and computer graphics," in ACM Multimedia, Singapore.

S. Dehnie, T. Sencar and N. Memon,. (2006). "Digital Image Forensics for Identifying Computer Generated and Digital Camera Images," in IEEE International Conference on image processing.

A. E. Dirik, S. Bayram, H. T. Sencar and N. Memon, (2007). "New features to identify computer generated images," in IEEE International Conference on Image Processing 4.

X. Kang, E. Zhang, Y. Chen and Y. Wei, (2011). "Forensic discrimination of computer generated images and photographs using spectral correlations in wavelet domain," Energy Procedia, vol. 13, no. 311, pp. 2174-2182.

F. Peng, Y. Zhu and M. Long, (2015). "Identification of Natural Images and Computer Generated Graphics using Multi-fractal Differences of PRNU," in ICA3PP 2015: Part II of the 15th International Conference on Algorithms and Architectures for Parallel Processing.

N. C. Woods and C. A. B. Robert, (2017) "A Model for Creating Exact Colour Spectrum for Image Forensic," University of Ibadan Journal of Science and Logics in ICT Research (UIJSLICTR), vol. Volume 1, no. 1, pp. 1-6.


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