@Article{csse.2022.022723,
AUTHOR = {Saleh Albahli},
TITLE = {Transfer Learning on Deep Neural Networks to Detect Pornography},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {43},
YEAR = {2022},
NUMBER = {2},
PAGES = {701--717},
URL = {http://www.techscience.com/csse/v43n2/47423},
ISSN = {},
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.},
DOI = {10.32604/csse.2022.022723}
}