Malicious content filtering based on semantic features
Malicious content filtering based on semantic features
复制标题
基于语义特征的恶意内容过滤
DOI：
10.1145/1655925.1656071
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
S. Han
中科院分区：
文献类型：
--
作者：
Semin Kim;Hyun;Jaehyun Jeon;Yong Man Ro;S. Han
提出了一种基于语义特征的恶意内容过滤方法。在传统的基于内容的方法中，诸如颜色和纹理的低级特征被用于过滤恶意内容。但是，由于低层特征和全局概念之间存在语义鸿沟，很难检测到它们。本文将全局概念划分为若干语义特征。这些语义特征用于对恶意内容的全局概念进行分类。设计语义特征，构造语义分类器。在实验中，我们通过比较低层特征和语义特征来评估过滤恶意内容的性能。实验结果表明，该方法比仅使用低层特征的方法具有更好的性能。
This paper proposes a method to filtering malicious contents using semantic features. In conventional content based approach, low-level features such as color and texture are used to filter malicious contents. But, it is difficult to detect them because of semantic gaps between the low-level features and global concepts. In this paper, global concepts are divided into several semantic features. These semantic features are used to classify the global concept of malicious contents. We design semantic features and construct semantic classifier. In experiment, we evaluate the performance to filter malicious contents by comparing low-level features and semantic features. Results show that our proposed method has better performance than the method using only low-level features.