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Transfer Learning on Deep Neural Networks to Detect Pornography

Saleh Albahli*

Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia

* Corresponding Author: Saleh Albahli. Email: email

Computer Systems Science and Engineering 2022, 43(2), 701-717. https://doi.org/10.32604/csse.2022.022723

Abstract

While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts both pornographic and non-pornographic contents. The videos were from different ethnicities and skin color which ensures the models can detect any kind of video. A VGG16, Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49, 0.49 and 0.78 respectively. Finally, utilizing transfer learning, a convolutional neural network was designed and yielded an accuracy of 0.98.

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Cite This Article

APA Style
Albahli, S. (2022). Transfer learning on deep neural networks to detect pornography. Computer Systems Science and Engineering, 43(2), 701-717. https://doi.org/10.32604/csse.2022.022723
Vancouver Style
Albahli S. Transfer learning on deep neural networks to detect pornography. Comput Syst Sci Eng. 2022;43(2):701-717 https://doi.org/10.32604/csse.2022.022723
IEEE Style
S. Albahli, "Transfer Learning on Deep Neural Networks to Detect Pornography," Comput. Syst. Sci. Eng., vol. 43, no. 2, pp. 701-717. 2022. https://doi.org/10.32604/csse.2022.022723



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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