Automated Nudity Recognition using Very Deep Residual Learning Network
Rasoul Banaeeyan1, Hezerul Abdul Karim2, Haris Lye3, Mohamad Faizal Ahmad Fauzi4, Sarina Mansor5, John See6
1Rasoul Banaeeyan, Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia.
2Hezerul Abdul Karim, Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia.
3Haris Lye, Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia.
4Mohamad Faizal Ahmad Fauzi, Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia.
5Sarina Mansor, Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia.
6John See, Faculty of Computing Informatics, Multimedia University, Cyberjaya, Malaysia.
Manuscript received on 26 September 2019 | Revised Manuscript received on 05 October 2019 | Manuscript Published on 22 October 2019 | PP: 136-141 | Volume-8 Issue-3S October 2019 | Retrieval Number: C10241083S19/2019©BEIESP | DOI: 10.35940/ijrte.C1024.1083S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 .
Keywords: Convolutional Neural Network, Deep Learning, Nudity Recognition, Residual Learning Block.
Scope of the Article: Deep Learning