Abstract
A precursor to successful automatic child exploitation material recognition is the ability to automatically identify pornography (largely solved) involving children (largely unsolved). Identifying children’s faces in images previously labelled as pornographic can provide a solution. Automatic child face detection plays an important role in online environments by facilitating Law Enforcing Agencies (LEA) to track online child abuse, bullying, sexual assault, but also can be used to detect cybercriminals who are targeting children to groom up them with a view of molestation later. Previous studies have investigated this problem in an attempt to identify only children faces from a pool of adult faces, which aims to extract information from the basic low- and high-level features i.e., colour, texture, skin tone, shape, facial structures etc. on child and adult faces. Typically, this is a machine learning-based architecture that accomplish a categorization task with the aim of identifying a child face, given a set of child and adult faces using classification technique based on extracted features from the training images. In this paper, we present a deep learning methodology, where machine learns the features straight away from the training images without having any information provided by humans to identify children faces. Compared to the results published in a couple of recent work, our proposed approach yields the highest precision and recall, and overall accuracy in recognition.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kwon YH, da Lobo Vitoria N (1993) Locating facial features for age classification. In: Proceedings of SPIE – the International Society for Optical Engineering, vol 2055. SPIE – The International Society for Optical Engineering, Bellingham, pp 62–72
Kwon YH, Lobo N (1999) Age classification from facial images. Comput Vis Image Underst 74(1):1–21
Hayashi J et al (2001) Method for estimating and modeling age and gender using facial image processing. In: Seventh international conference on virtual systems and multimedia. IEEE Computer Society, Los Alamitos, pp 439–448
Lanitis A et al (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern B 34(1):621–628
Cootes TF et al (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23:681–684
Yan S et al (2007) Auto-structured regressor from uncertain labels. In: International conference on computer vision
Khoa L et al (2009) Age estimation using active appearance models and support vector machine regression, biometrics: theory, applications, and systems, 2009. BTAS ’09. IEEE 3rd international conference. Washington, USA, pp 1–5
Geng X et al (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell 29(12):2234–2240
Fu Y, Huang TS (2008) Human age estimation with regression on discriminative aging manifold. IEEE Trans Multimed 10(4):578–584
Guo G et al (2008) Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans Image Process 17(7):1178–1188
Yan S et al (2009) Synchronized submanifold embedding for person independent pose estimation and beyond. IEEE Trans Image Process 18(1):202–210
Krizhevsky A (2012) ImageNet classification with deep convolutional neural networks, In: Advances in neural information processing systems, pp 1097–1105
NCMEC, National Center for Missing & Exploited Children (NCMEC). http://www.missingkids.com
Face and Gesture Recognition Research Network. http://www.fgnet.rsunit.com/
Simon B (2017) Large age-gap face verification by feature injection in deep networks. Pattern Recogn Lett 90. https://doi.org/10.1016/j.patrec.2017.03.006
Chang CC, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2 (1):27. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Islam M et al (2011) Child face detection using age specific luminance invariant geometric descriptor. In: 2011 IEEE international conference on signal and image processing applications (ICSIPA2011)
Watters PA (2018) Investigating malware epidemiology and child exploitation using algorithmic ethnography. In: Proceedings of the 51st Hawaii International Conference on Systems Science (HICSS), Hawaii
Watters PA (2018) Modelling the efficacy of auto-internet warnings to reduce demand for child exploitation material. In: Proceedings of the 22nd Asia-Pacific conference on knowledge discovery and data mining workshops
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Islam, M., Mahmood, A.N., Watters, P., Alazab, M. (2019). Forensic Detection of Child Exploitation Material Using Deep Learning. In: Alazab, M., Tang, M. (eds) Deep Learning Applications for Cyber Security. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-13057-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-13057-2_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13056-5
Online ISBN: 978-3-030-13057-2
eBook Packages: Computer ScienceComputer Science (R0)