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http://dx.doi.org/10.9717/kmms.2018.21.8.858

Implementation and Verification of Multi-level Convolutional Neural Network Algorithm for Identifying Unauthorized Image Files in the Military  

Kim, Youngsoo (Dept. of Computer & Information Engineering, Korea Air Force Academy)
Publication Information
Journal of Korea Multimedia Society / v.21, no.8, 2018 , pp. 858-863 More about this Journal
Abstract
In this paper, we propose and implement a multi-level convolutional neural network (CNN) algorithm to identify the sexually explicit and lewdness of various image files, and verify its effectiveness by using unauthorized image files generated in the actual military. The proposed algorithm increases the accuracy by applying the convolutional artificial neural network step by step to minimize classification error between similar categories. Experimental data have categorized 20,005 images in the real field into 6 authorization categories and 11 non-authorization categories. Experimental results show that the overall detection rate is 99.51% for the image files. In particular, the excellence of the proposed algorithm is verified through reducing the identification error rate between similar categories by 64.87% compared with the general CNN algorithm.
Keywords
Multi-level CNN; Convolutional Neural Network; Image Processing; Intelligent Information System; Military Application;
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