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Mobile Information Systems
Volume 2016 (2016), Article ID 3919134, 8 pages
Research Article

Learning-Based Detection of Harmful Data in Mobile Devices

1Department of Digital Media, Anyang University, 22 Samdeok-ro, 37 Beon-gil, Manan-gu, Anyang 430-714, Republic of Korea
2School of Software, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 156-743, Republic of Korea

Received 29 December 2015; Accepted 15 March 2016

Academic Editor: Seung Yang

Copyright © 2016 Seok-Woo Jang and Gye-Young Kim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The Internet has supported diverse types of multimedia content flowing freely on smart phones and tablet PCs based on its easy accessibility. However, multimedia content that can be emotionally harmful for children is also easily spread, causing many social problems. This paper proposes a method to assess the harmfulness of input images automatically based on an artificial neural network. The proposed method first detects human face areas based on the MCT features from the input images. Next, based on color characteristics, this study identifies human skin color areas along with the candidate areas of nipples, one of the human body parts representing harmfulness. Finally, the method removes nonnipple areas among the detected candidate areas using the artificial neural network. The experimental results show that the suggested neural network learning-based method can determine the harmfulness of various types of images more effectively by detecting nipple regions from input images robustly.