Citation

Banaeeyan, Rasoul and Abdul Karim, Hezerul and Lye Abdullah, Mohd Haris and Ahmad Fauzi, Mohammad Faizal and Mansor, Sarina and See, John Su Yang (2019) Automated Nudity Recognition using Very Deep Residual Learning Network. International Journal of Recent Technology and Engineering, 8 (3S). pp. 136-141. ISSN 2277-3878

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Abstract

The exponentially growing number of pornographic material has brought many challenges to the modern daily life, particularly where children and minors have unlimited access to the internet. In Malaysia, all local and foreign films should obtain the suitability approval before distribution or public viewing, and this process of screening visual contents of all the TV channels imposes a huge censorship cost to the service providers such as Unifi TV. To leverage this issue, this paper proposes to use an emerging model of Deep Learning (DL) techniques called Residual Learning Convolutional Neural Networks (ResNet), in order to automate the process of nudity detection in visual contents. The pre-trained ResNet model, with hundred and one layers, was utilized to perform transfer learning and solve a new binary classification problem of nudity versus non-nudity. The performance of the proposed model is evaluated based on a newly created dataset comprising more than 4k samples of nudity and non-nudity images. After conducting experiments on the nudity dataset, the deep learning method succeeded to achieve the best performance of 70.42% in term of F-score, 84.04% in term of accuracy, and 93.72% in term of AUC .

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network, deep learning, nudity recognition, residual learning block
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	07 Sep 2021 08:13
Last Modified:	07 Sep 2021 08:13
URII:	http://shdl.mmu.edu.my/id/eprint/8755

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