Identification of Natural Images and Computer-Generated Graphics Based on Statistical and Textural Features

Journal of Forensic Sciences

Volumn 60, Issue 2, 2015, Pages 435-443

  Peng, Fei ^a   Li, Jiao ting ^a   Long, Min ^b  

^a HUNAN UNIVERSITY   (China)

^b CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Computer generated graphics; Forensic science; Image source identification; Lacunarity analysis; Multifractal dimension; Natural images

Indexed keywords

EID: 84924094828     PISSN: 00221198     EISSN: 15564029     Source Type: Journal    
DOI: 10.1111/1556-4029.12680     Document Type: Article

References (24)

1. Chen DM, Li JH, Wang SL, Li SH. Identifying computer generated and digital camera images using fractional lower order moments. Proceedings of the Fourth IEEE Conference on Industrial Electronics and Applications, 2009 (ICIEA 2009); 2009 May 25-27; Xi'an, People's Republic of China. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2009;230-5.
2. Li CT. Source camera identification using enhanced sensor pattern noise. IEEE Trans Inf Forensics Secur 2010;5(2):280-7.
3. Li CT, Li Y. Colour decoupled photo response non-uniformity for digital image forensics. IEEE Trans Circuits Syst Video Technol 2012;22(2):260-71.
4. Dirik AE, Memon N. Image tamper detection based on demosaicing artifacts. Proceedings of the 16th IEEE International Conference on Image Processing (ICIP); 2009 Nov 7-10; Cairo, Egypt. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2009;1497-500.
5. Zhou LN, Wang DM. Digital image forensic techniques. Beijing, China: Beijing University, 2008.
6. Farid H, Lyu S. How realistic is photorealistic? IEEE Trans Signal Process 2005;53(2):845-50.
7. Guo K, Wang R. An effective method for identifying natural images and computer graphics. J Comput Inform Syst 2010;6(10):3303-8.
8. Ng TT, Chang SF. Physics-motivated features for distinguishing photographic images and computer graphics. Proceedings of the 13th Annual ACM International Conference on Multimedia (MM '05); 2005 November 6-12; Singapore. New York, NY: ACM, 2005;239-48; http://www.ee.columbia.edu/ln/dvmm/publications/05/ng_acmmm05.pdf.
9. Pan F, Chen JB, Huang JW. Discriminating between photorealistic computer graphics and natural images using fractal geometry. Sci China Ser F: Info Sci 2009;52(2):329-37.
10. Yang GQ, Cui RY. Method for natural image discrimination based on texture feature. Comput Appl Res 2010;27(7):2784-5.
11. Lukáš J, Fridrich J, Goljan M. Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 2006;1(2):205-14.
12. Lukáš J, Fridrich J, Goljan M. Determining digital image origin using sensor imperfections. Proc. SPIE 5685, Image and Video Communications and Processing. Bellingham, WA: SPIE, 2005;249-60; doi: 10.1117/12.587105.
13. Lukáš J, Fridrich J, Goljan M. Detecting digital image forgeries using sensor pattern noise. Proc. SPIE 6072, Security, Steganography, and watermarking of Multimedia Contents VIII, 60720Y. Bellingham, WA: SPIE, 2006;362-72; doi: 10.1117/12.640109.
14. Khanna N, Chiu GTC. Forensic techniques for classifying scanner. Proceedings of the 2008 IEEE International Conference on Computer Generated and Digital Camera Images, Acoustics, Speech and Signal Processing (ICASSP 2008); 2008 March 31-April 4; Las Vegas, NV. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2008;1653-6.
15. Khannaa N, Aravind K, George M, Chiu TC, Allebach P, Delp EJ. Forensic classification of imaging sensor types. Proc. SPIE 6505, Security, Steganography and Watermarking of Multimedia Contents IX. Bellingham, WA: SPIE, 2007;6505; doi: 10.1117/12.705849.
16. Peng F, Liu J. Identification of natural images and computer generated graphics based on hybrid features. Int J Digit Crime Forensics 2012;4(1):1-16.
17. Plotnick RE, Gardner RH, O'Neill RV. Lacunarity indices as measures of landscape texture. Landscape Ecol 1993;8(3):201-11.
18. Gilmore S, Hofmann-Wellenh R, Muir J, Soyer HP. Lacunarity analysis: a promising method for the automated assessment of melanocytic naevi and melanoma. PLoS ONE 2009;4(10):e7449.
19. Valous NA, Mendoza F, Sun DW, Allen P. Texture appearance characterization of pre-sliced pork ham images using fractal metrics: fourier analysis dimension and lacunarity. Food Res Int 2009;42(3):353-62.
20. Roy A, Perfect E, Dunne WM, Odling N, Kim JW. Lacunarity analysis of fracture networks: evidence for scale-dependent clustering. J Struct Geol 2010;32(10):1444-9.
21. Nilsson E. Multifractal-based image analysis with applications in medical imaging [M.S. thesis]. Umea, Sweden: Dept. Computing Science, Umea Univ, 2007.
22. Akansu AN, Serdijn WA, Selesnick IW. Wavelet transforms in signal processing: a review of emerging applications. Physical Commun 2010;3:1-18.
23. Corinna C, Vladimir V. Support-vector networks. Mach Learn 1995;20(3):273-97.
24. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011;2(3):27.