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A Study of Detecting Child Pornography on Smart Phone

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Advances in Network-Based Information Systems (NBiS 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 7))

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Abstract

Child Pornography is an increasingly visible rising cybercrime in the world today. Over the past decade, with rapid growth in smart phone usage, readily available free Cloud Computing storage, and various mobile communication apps, child pornographers have found a convenient and reliable mobile platform for instantly sharing pictures or videos of children being sexually abused. Within this new paradigm, law enforcement officers are finding that detecting, gathering, and processing evidence for the prosecution of child pornographers is becoming increasingly challenging. Deep learning is a machine learning method that models high-level abstractions in data and extracts hierarchical representations of data by using a deep graph with multiple processing layers. This paper presents a conceptual model of deep learning approach for detecting child pornography within the new paradigm by using log analysis, file name analysis and cell site analysis which investigate text logs of events that have happened in the smart phone at the scene of the crime using physical and logical acquisition to assists law enforcement officers in gathering and processing child pornography evidence for prosecution. In addition, this paper shows an illustrative example of logical and physical acquisition on smart phones using forensics tools.

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Acknowledgement

This work was supported by the Research Office - Zayed University, Abu Dhabi, United Arab Emirates, under Research Incentive Funds# R16096, R15048 and Cluster Award project# R16083.

Author information

Authors and Affiliations

  1. College of Technological Innovation, Zayed University, Abu Dhabi, UAE

    Farkhund Iqbal

  2. College of Technological Innovation, Zayed University, Dubai, UAE

    Andrew Marrington

  3. Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, Canada

    Patrick C. K. Hung, Shih-Chia Huang & Benjamin Yankson

  4. Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan

    Patrick C. K. Hung, Jing-Jie Lin, Guan-Pu Pan & Shih-Chia Huang

Authors
  1. Farkhund Iqbal
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  2. Andrew Marrington
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  3. Patrick C. K. Hung
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  4. Jing-Jie Lin
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  5. Guan-Pu Pan
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  6. Shih-Chia Huang
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  7. Benjamin Yankson
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Corresponding author

Correspondence to Patrick C. K. Hung .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Rissho University, Tokyo, Japan

    Tomoya Enokido

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

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Iqbal, F. et al. (2018). A Study of Detecting Child Pornography on Smart Phone. In: Barolli, L., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-65521-5_32

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  • DOI: https://doi.org/10.1007/978-3-319-65521-5_32

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