Method for detecting image spam email by picture character and local invariant feature

Abstract

The invention provides a method for detecting an image spam email by local invariant features of pictures, which can extract the invariant region feature of junk information in the pictures by using a scale-invariant feature conversion algorithm and extract characters embedded into the pictures to classify the pictures so as to form a feature vector library of the pictures combining two features together. Experiments prove that the recall rate of the spam email can be improved and the program operation time and space can be saved. The method can extract the invariant region feature in the pictures to generate the feature vectors of the pictures, and a support vector machine classifier is used for training and testing. In the method, by utilizing the text messages embedded into the pictures, the text string in the pictures can be excavated by using a graphic character recognition technology and the string can be taken as the feature of the pictures, and the Bayesian classifier is used for training and testing. The feature vector of each picture is composed of the local invariant feature of the picture and the text string; and two types of classifiers are used for classifying by a stacking method to achieve the purpose of detecting the image spam email.

Description

technical field [0001] The present invention is a combination of local invariant features of spam pictures and text embedded in pictures, using different classification methods, and integrating the two results, to realize the detection of image-type spam, which mainly solves the problem of image-type spam in today's technology. The problems of spam detection efficiency and low recall rate belong to the field of data mining and machine learning. Background technique [0002] E-mail has become an important way for people to communicate on the Internet, but due to the huge commercial, economic and political interests, the amount of spam has expanded rapidly. The image-based spam that was prevalent at the beginning was to embed spam information such as advertisements in the image in the form of text. Hrishikesh et al. are using the mined text and color features to classify the mail. In 2006, Fumera et al. proposed an OCR (Optical Character Recognition) technology to detect the ...

AI Technical Summary

Problems solved by technology

Distinguish junk pictures by calculating the threshold value. Although this method uses statistical knowledge to calculate more accurately, the amount of calculation is too large, and the time complexity of the algorithm is high, which is not conducive to practical applications.

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