Open AccessColor Model Based Convolutional Neural Network for Image Spam Classification
Author(s) -
Ahmad Mahdi Salih,
Ban N. Dhannoon
Publication year - 2020
Publication title -
al-nahrain journal of science
Language(s) - English
Resource type - Journals
eISSN - 2663-5461
pISSN - 2663-5453
DOI - 10.22401/anjs.23.4.08
Subject(s) - spamming , computer science , convolutional neural network , artificial intelligence , image (mathematics) , optical character recognition , pattern recognition (psychology) , machine learning , data mining , the internet , world wide web
For most of people, e-mail is the preferable medium for official communication. E-mail service providers face an endless challenge called spamming. Spammingis the exploitation of e-mail systems to send a bulk of unsolicited messages to a large number of recipients. Noisy image spamming is one of the new techniques to evade text analysis based and Optical Character Recognition (OCR) based spams filtering. In the present paper, Convolutional Neural Network (CNN) based on different color models was considered to address image spam problem. The proposed method was evaluated over a public image spam dataset. The results showed that the performance of the proposed CNN was affected by the color model used. The results also showed that XYZ model yields the best accuracy rate among all considered color models.