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Forensic Image Classification using Data Mining Decision Tree
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 Title & Authors
Forensic Image Classification using Data Mining Decision Tree
RHEE, Kang Hyeon;
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 Abstract
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.
 Keywords
Forensic image;GLCM;PPCA;TP and NP;AUROC;Minimum average decision error;
 Language
Korean
 Cited by
 References
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