基于meta标签和深度学习的色情网站侦测方法

A METHOD OF PORNOGRAPHIC WEBSITE DETECTION BASED ON META TAGS AND DEEP LEARNING

  • 摘要: 为了解决色情网站日益伪装隐蔽的问题,对色情网站文本内容进行分析。目前的研究多关注图片而忽略了对文本的细粒度分析,未有效利用文本信息对色情网站进行侦测。为解决上述问题,对HTML源代码进行下钻分析,提出基于meta标签的色情网站侦测方法,构建批量化色情网站侦测模型MPWM。对比基准模型,MPWM模型在精确率、召回率和F1值三个指标方面均取得了最佳的侦测效果,F1值达到了97.62%。

     

    Abstract: In order to solve the problem that pornographic websites are increasingly disguised and hidden, the text content of pornographic websites is analyzed. The current research focuses more on images and ignores the fine granularity analysis of text, and does not effectively use text information to detect pornographic websites. In order to solve the above problems, the HTML source code was drilled down, a method of pornographic website detection based on meta tags was proposed, and a mass pornographic website detection model (MPWM) was constructed. Compared with the benchmark model, MPWM model achieved the best detection effect in accuracy, recall and F1 value, and F1 value has reached 97.62%.

     

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