Color Channel Characteristics (CCC) for Efficient Digital Image Forensics


  • S. Gupta Computer Science & Engineering Faculty, I. K. Gujral Punjab Technical University, Kapurthala, India
  • N. Mohan Computer Science & Engineering Faculty, I. K. Gujral Punjab Technical University, Kapurthala, India
Volume: 8 | Issue: 1 | Pages: 2555-2561 | February 2018 |


Digital image forgery has become extremely easy as low-cost image processing programs are readily available. Digital image forensics is a science of classifying images as authentic or manipulated. This paper aims at implementing a novel digital image forensics technique by exploiting an image’s Color Channel Characteristics (CCC). The CCCs considered are the noise and edge characteristics of the image. Averaging, median, Gaussian and Wiener filters along with Sobel, Canny, Prewitt and Laplacian of Gaussian (LoG) edge detectors are applied to get the noise and texture features. A complete, no reference, blind classifier for image tamper detection has been proposed and implemented. The proposed CCC classifier can detect copy-move as well as image splicing accurately with lower dimensionality. Support Vector Machine is used for classification of images as authentic or tampered. Experimental results have shown that the proposed technique outperforms the existing ones and may serve as a complete tool for digital image forensics.


Download data is not yet available.


A. Rocha, W. Scheirer, T. Boult, S. Goldenstein, “Vision of the unseen: current trends and challenges in digital image and video forensics”, ACM Computer Survey, Vol. 43, No. 4, pp. 26:1–42, 2011

H. Farid, “Detecting digital forgeries using bispectral analysis”, Technical Report AIM-1657, AI Lab, Massachusetts Institute of Technology, 1999

G. K. Birajdar, V. H. Mankar, “Digital image forgery detection using passive techniques: A survey”,DigitalInvestigation,Vol. 10, No. 3, pp. 226-245, 2013 DOI:

X. Li, “Blind image quality assessment”, IEEE International Conference on Image Processing,Vol. 1, pp. I-449-I-452, 2002

I. Avcibas, S. Bayram, N. Memon, M. Ramkumar, B. Sankur, “A classifier design for detecting image manipulations”, International Conference on Image Processing, pp. 2645–2648, 2004

C. Popescu, H. Farid, Exposing digital forgeries by detecting duplicated image regions, Technical Report TR2004-515, Dartmouth College, 2004

S. Bayram, I. Avcibas, B. Sankur, N. Memon, “Image manipulation detection”, Electron Imaging, Vol. 15, No. 4,pp. 041102:1–17, 2006 DOI:

H. Gou, A. Swaminathan, M. Wu, “Noise features for image tampering detection and steganalysis”, International Conference on Image Processing, pp. 97–100, 2007 DOI:

Z. Zhang, J. Kang, Y. Ren, “An effective algorithm of image splicing detection”, International Conference on Computer Science and Software Engineering, pp. 1035–1039, 2008 DOI:

B. Mahdian, S. Saic, “Using noise inconsistencies for blind image forensics”, Image and Vision Computing, Vol. 27, No. 10, pp. 1497-1503, 2009 DOI:

W. Wang, J. Dong, T. Tan, “Effective image splicing detection based on image chroma”, 16thIEEE International Conference on Image Processing, pp. 1257-1260, 2009 DOI:

F. Battisti, M. Carli, A. Neri,“Image forgery detection by means of no-reference quality metrics”, IS&T/SPIE Electronic Imaging, International Societyfor Optics and Photonics, pp. 83030K-83030K, 2012 DOI:

Y. Ke, Q. Zhang, W. Min, S. Zhang, “Detecting Image Forgery Based on Noise Estimation”, International Journal of Multimedia and Ubiquitous Engineering, Vol. 9, No. 1, pp. 325-336, 2014 DOI:

B. Liu, C. M. Pun, “Splicing Forgery Exposure in Digital Image by Detecting Noise Discrepancies”, International Journal of Computer and Communication Engineering, Vol. 4, No. 1, pp. 33-37, 2015 DOI:

Y. Q. Shi, C. Chen, W. Chen, “A natural image model approach to splicing detection”, 9th Workshop on Multimedia & Security, pp. 51–62, 2007 DOI:

Z. He, W. Lu, W. Sun, J. Huang, “Digital image splicing detection based on Markov features in DCT and DWT domain”, Pattern Recognition, Vol. 45, No. 12, pp. 4292-4299, 2012 DOI:

G. Muhammad, M. S. Dewan, M. Moniruzzaman, M. Hussain, M. N. Huda, “Image forgery detection using Gabor filters and DCT”, IEEE International Conference on Electrical Engineering and Information & Communication Technology, pp. 1-5, 2014 DOI:

M. Hussain, S. Qasem, G. Bebis, G. Muhammad, H.Aboalsamh, H. Mathkour, “Evaluation of image forgery detection using multi-scale Weber local descriptors”, International Journal on Artificial Intelligence Tools, Vol. 24, No. 4, pp. 1540016-1540043, 2015 DOI:

X. Shen, Z. Shi, H. Chen, “Splicing image forgery detection using textural features based on the grey level co-occurrence matrices”, IET Image Processing, Vol. 11, No. 1, pp. 44-53, 2016 DOI:

I. Amerini, L. Ballan, R. Caldelli, A. DelBimbo, G. Serra, “A SIFT-based forensic method for copy–move attack detection and transformation recovery”, IEEE Transactions on Information Forensics and Security, Vol. 6, No. 3, pp. 1099-1110, 2011 DOI:

J. Canny, “A computational approach to edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, pp. 679-698, 1986 DOI:

R. M. Haralick, “Statistical and structural approaches to texture”, Proceedings of the IEEE, Vol. 67, No. 5, pp. 786-804, 1979 DOI:

J. Dong, W. Wang, “CASIA tampered image detection evaluation database”, IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), July 6-10, 2013 DOI:

C. Chang, C. J. Lin, “LIBSVM: a library for support vector machines”, ACM Transactions on Intelligent Systems and Technology, Vol. 2, pp. 27:1-27, 2011


How to Cite

S. Gupta and N. Mohan, “Color Channel Characteristics (CCC) for Efficient Digital Image Forensics”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 1, pp. 2555–2561, Feb. 2018.


Abstract Views: 590
PDF Downloads: 289

Metrics Information
Bookmark and Share