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Authors: André Tabone 1 ; Alexandra Bonnici 1 ; Stefania Cristina 1 ; Reuben Farrugia 2 and Kenneth Camilleri 1

Affiliations: 1 Department of Systems and Control, University of Malta, Malta ; 2 Department of Computer Engineering, University of Malta, Malta

Keyword(s): Deep Neural Networks, Pornographic Detection, Classifiers, Private Body Part Detector.

Abstract: Fast and accurate detection of sexually exploitative imagery is necessary for law enforcement agencies to allow for prosecution of suspect individuals. In literature, techniques which can be used to assist law enforcement agencies only determine whether the image content is pornographic or benign. In this paper, we provide a review on classical handcrafted-feature based and deep-learning based pornographic detection in images and describe a framework which goes beyond this, to identify the location of genitalia in the image. Despite this being a computationally complex task, we show that by learning multiple features, a MobileNet framework can achieve an accuracy of 76.29% in the correct labelling of female and male sexual organs.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Tabone, A.; Bonnici, A.; Cristina, S.; Farrugia, R. and Camilleri, K. (2020). Private Body Part Detection using Deep Learning. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM, ISBN 978-989-758-397-1; ISSN 2184-4313, pages 205-211. DOI: 10.5220/0009101502050211

@conference{icpram20,
author={André Tabone. and Alexandra Bonnici. and Stefania Cristina. and Reuben Farrugia. and Kenneth Camilleri.},
title={Private Body Part Detection using Deep Learning},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM,},
year={2020},
pages={205-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009101502050211},
isbn={978-989-758-397-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM,
TI - Private Body Part Detection using Deep Learning
SN - 978-989-758-397-1
IS - 2184-4313
AU - Tabone, A.
AU - Bonnici, A.
AU - Cristina, S.
AU - Farrugia, R.
AU - Camilleri, K.
PY - 2020
SP - 205
EP - 211
DO - 10.5220/0009101502050211

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