ASM-Based Objectionable Image Detection in Social Network Services

Abstract

This paper presents a method for detecting harmful images using an active shape model (ASM) in social network services (SNS). For this purpose, our method first learns the shape of a woman's breast lines through principal component analysis and alignment, as well as the distribution of the intensity values of the corresponding control points. This method then finds actual breast lines with a learned shape and the pixel distribution. In this paper, to accurately select the initial positions of the ASM, we attempt to extract its parameter values for the scale, rotation, and translation. To obtain this information, we search for the location of the nipple areas and extract the location of the candidate breast lines by radiating in all directions from each nipple position. We then locate the mean shape of the ASM by finding the scale and rotation values with the extracted breast lines. Subsequently, we repeat the matching process of the ASM until saturation is reached. Finally, we determine objectionable images by calculating the average distance between each control point in a converged shape and a candidate breast line.

Harmful Content Detection Based on Cascaded Adaptive Boosting

“…In addition to the various methods described above, many new methods related to the detection of adult images are still being introduced [16]. Most of these methods still use a human skin color distribution model.…”

“…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].…”