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Research on the Image Spam Filtering Technology Based on Content

Author: LiuFen
Tutor: ShuaiJianMei
School: University of Science and Technology of China
Course: Pattern Recognition and Intelligent Systems
Keywords: Image spam Feature Extraction LS-SVM
CLC: TP393.098
Type: Master's thesis
Year: 2010
Downloads: 92
Quote: 2
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Abstract


Email extensive applications bring convenience to people's communication, its by-products - spam, but also to the people's work life brought great hardship. A lot of spam is not only a waste of our time and energy, while those that contain viruses, Trojan spam tremendous threat to the security of our network and computer systems. Scholars since the 1990s, a lot of research and has put forward a number of text message-based spam filtering solutions. In order to circumvent the filtering mechanism, more and more spam purpose of the message embedded in the image, image spam (referred to as the I-spam). According to the the Symantec spam status report, in early May 2009, image spam has accounted for 20% of all spam. Image spam filtering is particularly important. Differences for spam image with normal mail images, content-based image spam filtering method and the hierarchical modular image spam filtering system as the basis for design, image spam filtering. The contribution of this paper is mainly in the following aspects: (1) analysis of the basic characteristics of the image, the proposed use of the gradient and the gradient direction characteristics of image spam filtering. Compared with the basic color characteristics as well as meta-data characteristics of the image gradient and the gradient direction can be a good reflection of the object gray-scale variation of the image has a relatively high recognition rate proved by experiments these two features. (2) the use of hierarchical modular image spam filtering system, the combination of the characteristic level and class of filter combinations, use of similar characteristics feature level combination of different types of features a combination of filter-class. Experiments show that the proposed method can avoid overfitting, and be able to make full use of the advantages of the different characteristics of training filter to achieve a high recognition rate. (3) the LS-SVM algorithm is applied to image spam filtering. LS-SVM algorithm is the SVM an improved algorithm has been applied to the regression, classification and prove its performance is better than the SVM algorithm. This article proved by experiments in image spam filtering LS-SVM algorithm has a better effect than other algorithms.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > The application of computer network > E-mail ( E -mail )
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