用CNN和VGG-16对比识别色情图像，CNN更准更快。
Comparison of deep learning models: CNN and VGG-16 in identifying pornographic content
- 采用CNN与VGG-16模型对比检测网页色情图像。
- CNN在50轮训练、学习率0.001下准确率达94.87%。
- 适合内容审核系统开发者参考部署方案。
2020年，印度尼西亚政府因负面内容（包括色情）封锁了总计59,741个网站，其中14,266个属于此类。然而公众仍可通过虚拟私人网络（VPNs）访问这些被封网站。这促使研究快速识别色情内容的方法。本研究旨在开发一种基于深度学习的系统，利用卷积神经网络（CNN）和视觉几何组16（VGG-16）模型识别疑似含色情图像的网站。通过全面比较两种模型的表现，结果表明：在第8次实验中，使用50个训练轮次和0.001学习率的CNN模型达到最佳测试准确率，为0.9487（即94.87%）。该结果说明，相比VGG-16模型，CNN在快速且准确地检测色情内容方面更具优势。
原文摘要 · Abstract (English)
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.
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