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http://dx.doi.org/10.5573/ieie.2016.53.12.111

Color Modification Detection Using Normalization and Weighted Sum of Color Components  

Shin, Hyun Jun (Dept. Electronics Eng., Pusan National University)
Jeon, Jong Ju (Dept. Electronics Eng., Pusan National University)
Eom, Il Kyu (Dept. Electronics Eng., Pusan National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.53, no.12, 2016 , pp. 111-119 More about this Journal
Abstract
Most commercial digital cameras acquire the colors of an image through the color filter array, and interpolate missing pixels of the image. Because of this fact, original pixels and interpolated pixels have different statistical characteristics. If colors of an image are modified, the color filter array pattern that consists of RGB channels is changed. Using this pattern change, a color forgery detection method were presented. The conventional method uses the number of pixels that exceeds the maximum or minimum value of pre-defined block by only exploiting green component. However, this algorithm cannot remove the flat area which is occurred when color is changed. And the conventional method has demerit that cannot detect the forged image with rare green pixels. In this paper, we propose an enhanced color forgery detection algorithm using the normalization and weighted sum of the color components. Our method can reduce the detection error by using all color components and removing flat area. Through simulations, we observe that our proposed method shows better detection performance compared to the conventional method.
Keywords
Color forgery; Color filter array; Demosaicing; Normalization; Weighted sum;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 B. Gunturk, J. Glotzbach, Y. Altunbasak, R. Schafer, and R. Mersereau, "Demosaicking: color filter array interpolation", IEEE Sig. Process. Magazine, pp. 44-54, 2005.
2 A. C. Popescu, and H. Farid, "Exposing digital forgeries in color filter array interpolated images", IEEE Trans. signal process, vol. 54, no. 10, pp. 3948-3959, 2005.
3 A. C. Kot, "Accurate detection of demosaicking regularity for digital image forensics", IEEE Trans. Inf. Forensics Secur, vol. 4, no. 4, pp. 899-910, 2009.   DOI
4 P. Ferrara, T. Bianchi, A. De Rosa, and A. Piva, "Image forgery localization via fine-grained analysis of CFA artifacts", IEEE Trans. Inf. Forensics Secur, vol. 7, no. 5, pp. 1566-1577, 2012.   DOI
5 C. H. Choi, J. H. Choi, and H. K. Lee, "CFA pattern identification of digital cameras using intermediate value counting", Proceedings of the Thirteenth ACM Multimedia Workshop on Multimedia and Security, MM&Sec '11, ACM, New York, NY, USA, pp. 21-26, 2011.
6 E. Chang, S. Cheung and D. Y. Pan. "Color filter array recovery using a threshold-based variable number of gradients", Proceedings of SPIE, Sensors, Cameras, and Applications for Digital Photography, pp. 36-43, 1999.
7 T. Gloe and R. Bohme, "The 'Dresden Image Database' for benchmarking digital image forensics", Proceedings of the 25th Symposium On Applied Computing (ACM SAC 2010), vol. 2, pp. 1585-1591, 2010.
8 G. Horvath, http://www.rawtherapee.com/
9 K. Hirakawa and T. W. Parks, "Adaptive homogeneity directed demosaicing algorithm", IEEE Trans. Image Process., vol. 14, no, 3 pp. 360-369, 2005.   DOI
10 E. Martinec and P. Lee, "AMAZE Demosaicing Algorithm", 2010. http://www.rawtherapee.com/.
11 L. Zhang and X. Wu, "Color demosaicking via directional linear minimum mean square-error estimation, " IEEE Trans. Image Process, vol. 14, no. 12, pp. 2167-2178, 2005.   DOI
12 C. Y. Tsai and K. T. Song, "Heterogeneity projection hard-decision color interpolation using spectral-spatial correlation", IEEE Trans. Image Process, vol. 16, no. 11, pp. 78-91, 2007.   DOI
13 Jacek Gozdz, DCB demosaicing algorithm. http://www.linuxphoto.org/html/dcb.html.
14 J. J. Jeon, S. H. Park, Y. I. Kim, and I. K. Eom, "Copy-rotate-move forged region detection using compensation of coordinate shift by rotation", Journal of KIIT, vol. 13, no. 10, pp. 51-58, 2015.
15 H. Farid, "A survey of image forgery detection", IEEE Signal Process Mag, vol. 2, no.26, pp. 16-25, 2009.
16 G. K. Birajdar, and V. H. Mankar, "Digital image forgery detection using passive techniques: A survey", Digital Invest, vol. 10, no. 3, pp. 226-245, 2013.   DOI
17 R. Davarzani, K. Yaghmaie, S. Mozaffari, and M. Tapak, "Copy-move forgery detection using multi resolution local binary patterns", Forensic science international, vol. 231, pp. 61-72, 2013.   DOI
18 X. Zhao, S. Wang, S. Li, and J. Li, " Passive miage-splicing detection by a 2-D noncausal Markov model", IEEE Trans. Circuits Syst. Video Technol, vol. 25, no. 2, pp. 185-199, 2015.   DOI
19 J. G. Han, T. H. Park, Y. H. Moon and I. K. Eom, "Efficient markov feature extraction method for image splicing detection using maximization and threshold expansion", Journal of Electronic Imaging, vol. 25, no. 2, pp. 023031-1-023031-8, 2016.   DOI
20 H. Cao and A. C. Kot, "Manipulation detection on image patches using FusionBoost", IEEE Trans. Inf. Forensics Secur., vol. 7, no. 3, pp. 992-1002, 2012.   DOI
21 B. G. Jeong, Y. H. Moon, and I. K. Eom, "Blind identification of image manipulation type using mixed statistical moments", Journal of Electronic Imaging, vol. 24, no. 1, pp. 013029-1-013029-12, 2015.   DOI
22 C. H. Choi, H. Y. Lee, and H. K. Lee, "Estimation of color modification in digital images by CFA pattern change", Forensic science international, vol. 226, pp. 94-105, 2013.   DOI
23 J. R. Seo and I. K. Eom, "Forged color region detection using color pattern decomposition and hypothesis test", Journal of IEIE, vol. 52, no. 7, pp. 77-85, 2015.