Comparison of deep learning models: CNN and VGG-16 in identifying pornographic content

TL;DR

This study compares CNN and VGG-16 deep learning models for rapid and accurate identification of pornographic content in websites, finding CNN more effective in terms of speed and accuracy.

Contribution

It provides a comprehensive comparison between CNN and VGG-16 models specifically for pornographic content detection, highlighting CNN's superior performance.

Findings

CNN achieved 94.87% accuracy at optimal settings.

CNN outperformed VGG-16 in detection speed and accuracy.

Optimal CNN parameters were 50 epochs and 0.001 learning rate.

Abstract

In 2020, a total of 59,741 websites were blocked by the Indonesian government due to containing negative content, including pornography, with 14,266 websites falling into this category. However, these blocked websites could still be accessed by the public using virtual private networks (VPNs). This prompted the research idea to quickly identify pornographic content. This study aims to develop a system capable of identifying websites suspected of containing pornographic image content, using a deep learning approach with convolutional neural network (CNN) and visual geometry group 16 (VGG-16) model. The two models were then explored comprehensively and holistically to determine which model was most effective in detecting pornographic content quickly. Based on the findings of the comparison between testing the CNN and VGG-16 models, research results showed that the best test results were obtained in the eighth experiment using the CNN model at an epoch value level of 50 and a learning rate of 0.001 of 0.9487 or 94.87%. This can be interpreted that the CNN model is more effective in detecting pornographic content quickly and accurately compared to using the VGG-16 model.