Advanced SearchSearch Tips
Forensic Image Classification using Data Mining Decision Tree
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Forensic Image Classification using Data Mining Decision Tree
RHEE, Kang Hyeon;
  PDF(new window)
In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as `Excellent(A)` because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.
Forensic image;GLCM;PPCA;TP and NP;AUROC;Minimum average decision error;
 Cited by
Kang Hyeon RHEE, "Image Forensic Decision Algorithm using Edge Energy Information of Forgery Image," IEEK, Journal No. 51(3), pp. 27-34, 2014.7.

Kang Hyeon RHEE, "Median Filtering Detection using Latent Growth Modeling," IEEK, Journal No. 52(1), pp. 61-68, Jan. 2015.

Haralick, K. Shanmugam and Its'hak Dinstein, "Textural Features for Image Classification," IEEE Trasn. on Systems, Man and Cybemetics, Vol. SMC-3, No. 6, pp. 610-621, November 1973. crossref(new window)

Wang, Li and He Dong Chen, "Texture Classification Using Texture Spectrum," Pattern Recognition, Vol. 23, No. 8, pp. 905-910, 1990. crossref(new window)

Pentland, A. P., "Fractal-based Description of Natural Science," IEEE Trans, on Pattern Analysis and Machine Intelligence, No. 6(6), pp. 661-674, 1984

Cross, G. R., and A. K. Jain, "Markov Random Field Texture Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-5, No. 1, pp. 25-39, 1983. crossref(new window)

Derin, H. and H. Elliot, "Modeling and Segmentation of Noisy Textured Images Using Gibbs Random Fields," IEEE Trans. Pattern Analysis and Machine Intelligence, No. 9(I), No. 39-55, 1987.


Junshi Xia, Jocelyn Chanussot, Peijun Du and Xiyan He, "(Semi-) Supervised Probabilistic Principal Component Analysis for Hyperspectral Remote Sensing Image Classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, Issue: 6, pp. 2224-2236, 2014. crossref(new window)


G. Cao, Y. Zhao, R. Ni, L. Yu, and H. Tian, "Forensic detection of median filtering in digital images," in Proc. 2010 IEEE Int. Conf. Multimedia and EXPO, pp. 89-94, Jul. 2010.

S. Wang, C. Lam, "Texture feature extraction using gray level gradient based co-occurrence matrices," IEEE International Conference on systems, Man, and Cybernetics, 1996, Vol. 1, pp. 267-271, 1996.