Transfer Learning on Deep Neural Networks to Detect Pornography  

作　　者：Saleh Albahli 

机构地区：[1]Department of Information Technology,College of Computer,Qassim University,Buraydah,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2022年第11期701-717,共17页计算机系统科学与工程（英文）

摘　　要：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.

关 键 词：Pornographic video detection classification convolutional neural network InceptionV3 Resnet50 VGG16 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]