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Detection of Harmful Content Using Multilevel Verification in Visual Sensor Data

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Abstract

Various types of harmful content such as adult images and video clips have been increasingly distributed through wired or wireless visual sensor-based networks. In this paper, we propose a new algorithm for extracting human nipple regions, representing the harmfulness of the images, by using a multilevel verification technique in visual sensor-based image data. The proposed algorithm first detects human face regions including eyes and lips from input images. The method then generates a nipple map utilizing representative features that female nipples have and detects candidate nipple regions by applying the generated nipple map to segmented skin regions followed by morphological operations. Subsequently, the proposed method selects real nipple areas after eliminating non-nipple regions at multiple levels by applying geometrical information and an average color filter to the detected candidate nipple regions. Experimental results show that the proposed method can robustly extract female nipple regions in various types of input images captured in environments where certain constraints are not imposed on.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (2011-0021984).

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Authors and Affiliations

  1. Department of Digital Media, Anyang University, 708-113, Anyang 5-Dong, Manan-Gu, Anyang, 430-714, Korea

    Seok-Woo Jang & Myunghee Jung

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  1. Seok-Woo Jang
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  2. Myunghee Jung
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Correspondence to Myunghee Jung.

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Jang, SW., Jung, M. Detection of Harmful Content Using Multilevel Verification in Visual Sensor Data. Wireless Pers Commun 86, 109–124 (2016). https://doi.org/10.1007/s11277-015-2966-1

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