Learning-Based Detection of Harmful Data in Mobile Devices

Abstract

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.

Harmful Content Detection Based on Cascaded Adaptive Boosting

“…A method of using the components of the human body [15] was proposed that automatically determines the presence or absence of harmfulness of an input image using an artificial neural network. In this method, the human face region is first detected based on the MCT feature from the input image.…”

“…In this method, if the number of harmful images included in the retrieved images is more than the predetermined number, the query image is determined as a harmful image. In [15], the input image is analyzed and it is detected whether an important component of the exposed human body such as nipples is included in the image to determine whether the image is harmful. In addition to the algorithms described above, various methods have been proposed to extract harmful contents more robustly [16].…”