Designing Light Filters To Detect Skin Using A Low-Powered Sensor

Keywords

filters; low-powered sensor; machine learning; nudity; skin detection

Abstract

Detection of nudity in photos and videos, especially prior to uploading to the internet, is vital to solving many problems related to adolescent sexting, the distribution of child pornography, and cyberbullying. The problem with using nudity detection algorithms on high fidelity images as a means to combat these problems is that: 1) it implies that a digitized nude photo of a minor already exists (i.e., child pornography), and 2) there are real ethical and legal concerns around the distribution and processing of child pornography. Once a camera captures an image, that image is no longer secure. Therefore, we need to develop new privacy-preserving solutions that prevent the digital capture of nude imagery of minors. Our research takes a first step in trying to accomplish this goal: In this paper, we examine the feasibility of using a low-powered sensor to detect skin dominance (defined as an image comprised of 50% or more of human skin tone) in a visual scene. By designing four custom light filters to enhance the digital information extracted from 300 scenes captured with the sensor (without digitizing high-fidelity visual features), we were able to accurately detect a skin dominant scene with 83.7% accuracy, 83% precision, and 85% recall. Our long-term goal is to design a low-powered vision sensor that can be mounted on a digital camera lens on a teen's mobile device to detect and/or prevent the capture of nude imagery. Thus, we discuss the limitations of this work toward this larger goal, as well as future research directions.

Publication Date

10-1-2018

Publication Title

Conference Proceedings - IEEE SOUTHEASTCON

Volume

2018-April

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/SECON.2018.8479027

Socpus ID

85056177159 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85056177159

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