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Home / programming language / Online Matlab Projects / Filtering Image-based Spam Using Multifractal Analysis and Active Learning Feedback-Driven Semi-Supervised Support Vector Machine
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Filtering Image-based Spam Using Multifractal Analysis and Active Learning Feedback-Driven Semi-Supervised Support Vector Machine

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SKU: PROJ3006 Categories: 2015 Projects, Final Year Projects, Digital Image Processing Projects, Online Matlab Projects Tags: academic projects, Andoid project 2015-2016, Android project 2013-2014, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project 2015-2016, dotnet project Abstract, dotnet project list, elysium technologies abstract, elysium technologies chennai, elysium technologies coimbatore, elysium technologies company, elysium technologies courses, elysium technologies erode, elysium technologies inpant traning, elysium technologies internship, elysium technologies jobs, elysium technologies madurai, elysium technologies mou, elysium technologies pondychery, elysium technologies projectlist, elysium technologies projects, elysium technologies ramnad, elysium technologies salem, elysium technologies software, elysium technologies tirunelveli, elysium technologies trichy, Final Year Projects, java project list, java projects 2013-2014, java projects 2015-2016, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project 2015-2016, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project Abstract, Php project list, php projects 2015-2016, Power Electronic project 2013-2014, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center coimbatore, project center Erode, project center Hyderabad, project center Kollam, project center madurai, project center Pandicherry, project center ramnad, project center Salem, project center Tiruneveli, project center trichy, research center, Students Projects, vlsi prjects 2015-2016, Vlsi project 2013-2014, Vlsi project Abstract, Vlsi project list
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Filtering Image-based Spam Using Multifractal Analysis and Active Learning Feedback-Driven Semi-Supervised Support Vector Machine

Abstract—Filtering Image-based Spam Using Multifractal Analysis and Active Learning Feedback-Driven Semi-Supervised Support Vector Machine. Traditional anti-spam technologies can’t block image-based spam because spammers employ a variety of image creation and randomization algorithms to make the message fully legible by the human eye but undistinguishable by the most anti-spam engines. In this paper we propose a novel composite method to filter image-based spam accurately and effectively, which can be easily implemented as a plug-in in SpamAssassin. Our method takes advantage of the two natures of image-based spams: large quantity, similarity and character variability. For the first nature, we use rules of SpamAssassin to detect the emails characteristic. If a new email has been identified as spam by the rules, it will be blocked. Otherwise, image-based mail will be captured by the plug-in. For the second nature,the plug-in will use multifractal analysis in multi-orientation wavelet pyramid algorithm to get image-based email texture descriptor which has strong invariance to many factors, use a hybrid filter-wrapper feature subset selection algorithm based on particle swarm optimization to reduce some redundant or irrelevant features in the texture descriptor, < Final Year Projects > and use a semi-supervised support vector machines classification algorithm to detect whether an email is ham or spam, then use active learning clustering to get the most representative emails for relabeling through user feedback. The relabeled emails by users feedback and the unlabeled suspect spams by SVM will be used to retrain the classification for improving accuracy of spam filter. The experimental results demonstrate that our method is of high efficiency, high accuracy and low false positive rate. The accuracy will be improved and the false positive rate will be reduced along with more and more retraining. So, the method is fit especially for an adversarial learning and processing like spam filtering.

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