Toward Detection of Child Exploitation Material

A Forensic Approach

Mofakharul Islam, Paul Watters, Abdun Mahmood, Mamoun Alazab

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

With continual advances in Internet capability, in addition to its global and decentralized nature, the Internet along with different social networking sites are experiencing a boom in demand and supply. Recent study found that the social networking sites like Facebook, Twitter, and MySpace are providing a forum for paedophiles to share child pornography. With the advent of sophisticated digital technology, Law Enforcement Agency (LEAs) around the world dealing with child pornography facing real challenge to combat with the technologically-savvy paedophiles. The major challenge in child pornography lies in authentic detection of children face in an image. The main objective of this research is to present a novel framework for a dedicated child face detection tool, where we will use child’s face specific contextual contexts and visual cues that are based on new knowledge in terms of features or contexts representatives of child’s skin and face. The proposed technique can estimate age categorically – adult or child based on a new hybrid feature descriptor, called Luminance Invariant & Geometrical Relation based Descriptor (LIGRD). LIGRD is composed of some low and high-level features, which are found to be effective in characterizing the local appearance in terms of chromaticity, texture, and geometric relational information of few facial visual cues simultaneously. Comparison of our experimental results with that of another recently published work reveals our proposed approach yields the highest precision and recall, and overall accuracy in recognition.
Original languageEnglish
Title of host publicationDeep Learning Applications for Cyber Security
EditorsMamoun Alazab, MingJian Tang
PublisherSpringer
Pages221-246
Number of pages26
ISBN (Electronic)978-3-030-13057-2
ISBN (Print)978-3-030-13056-5
DOIs
Publication statusPublished - 2019

Publication series

NameAdvanced Sciences and Technologies for Security Applications
PublisherSpringer International Publishing
ISSN (Print)1613-5113

Fingerprint

Internet
Law enforcement
Face recognition
Luminance
Skin
Textures

Cite this

Islam, M., Watters, P., Mahmood, A., & Alazab, M. (2019). Toward Detection of Child Exploitation Material: A Forensic Approach. In M. Alazab, & M. Tang (Eds.), Deep Learning Applications for Cyber Security (pp. 221-246). (Advanced Sciences and Technologies for Security Applications). Springer. https://doi.org/10.1007/978-3-030-13057-2_11
Islam, Mofakharul ; Watters, Paul ; Mahmood, Abdun ; Alazab, Mamoun. / Toward Detection of Child Exploitation Material : A Forensic Approach. Deep Learning Applications for Cyber Security. editor / Mamoun Alazab ; MingJian Tang. Springer, 2019. pp. 221-246 (Advanced Sciences and Technologies for Security Applications).
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Islam, M, Watters, P, Mahmood, A & Alazab, M 2019, Toward Detection of Child Exploitation Material: A Forensic Approach. in M Alazab & M Tang (eds), Deep Learning Applications for Cyber Security. Advanced Sciences and Technologies for Security Applications, Springer, pp. 221-246. https://doi.org/10.1007/978-3-030-13057-2_11

Toward Detection of Child Exploitation Material : A Forensic Approach. / Islam, Mofakharul; Watters, Paul; Mahmood, Abdun; Alazab, Mamoun.

Deep Learning Applications for Cyber Security. ed. / Mamoun Alazab; MingJian Tang. Springer, 2019. p. 221-246 (Advanced Sciences and Technologies for Security Applications).

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

TY - CHAP

T1 - Toward Detection of Child Exploitation Material

T2 - A Forensic Approach

AU - Islam, Mofakharul

AU - Watters, Paul

AU - Mahmood, Abdun

AU - Alazab, Mamoun

PY - 2019

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AB - With continual advances in Internet capability, in addition to its global and decentralized nature, the Internet along with different social networking sites are experiencing a boom in demand and supply. Recent study found that the social networking sites like Facebook, Twitter, and MySpace are providing a forum for paedophiles to share child pornography. With the advent of sophisticated digital technology, Law Enforcement Agency (LEAs) around the world dealing with child pornography facing real challenge to combat with the technologically-savvy paedophiles. The major challenge in child pornography lies in authentic detection of children face in an image. The main objective of this research is to present a novel framework for a dedicated child face detection tool, where we will use child’s face specific contextual contexts and visual cues that are based on new knowledge in terms of features or contexts representatives of child’s skin and face. The proposed technique can estimate age categorically – adult or child based on a new hybrid feature descriptor, called Luminance Invariant & Geometrical Relation based Descriptor (LIGRD). LIGRD is composed of some low and high-level features, which are found to be effective in characterizing the local appearance in terms of chromaticity, texture, and geometric relational information of few facial visual cues simultaneously. Comparison of our experimental results with that of another recently published work reveals our proposed approach yields the highest precision and recall, and overall accuracy in recognition.

KW - Child exploitation

KW - Child pornography

KW - Digital forensics

KW - Intelligence analysis

KW - Face detection

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DO - 10.1007/978-3-030-13057-2_11

M3 - Chapter

SN - 978-3-030-13056-5

T3 - Advanced Sciences and Technologies for Security Applications

SP - 221

EP - 246

BT - Deep Learning Applications for Cyber Security

A2 - Alazab, Mamoun

A2 - Tang, MingJian

PB - Springer

ER -

Islam M, Watters P, Mahmood A, Alazab M. Toward Detection of Child Exploitation Material: A Forensic Approach. In Alazab M, Tang M, editors, Deep Learning Applications for Cyber Security. Springer. 2019. p. 221-246. (Advanced Sciences and Technologies for Security Applications). https://doi.org/10.1007/978-3-030-13057-2_11