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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.


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.


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.
Vancouver Style
Albahli S. Transfer learning on deep neural networks to detect pornography. Comput Syst Sci Eng. 2022;43(2):701-717
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.

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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