Learning-Based Detection of Harmful Data in Mobile Devices
Joint Authors
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Telecommunications Engineering
Abstract EN
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.
American Psychological Association (APA)
Jang, Seok-Woo& Kim, Gye-Young. 2016. Learning-Based Detection of Harmful Data in Mobile Devices. Mobile Information Systems،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1111443
Modern Language Association (MLA)
Jang, Seok-Woo& Kim, Gye-Young. Learning-Based Detection of Harmful Data in Mobile Devices. Mobile Information Systems No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1111443
American Medical Association (AMA)
Jang, Seok-Woo& Kim, Gye-Young. Learning-Based Detection of Harmful Data in Mobile Devices. Mobile Information Systems. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1111443
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111443