Title

Convolutional neural networks for image spam detection

Publication Date

5-3-2020

Document Type

Article

Department

Computer Science

Publication Title

Information Security Journal

Volume

29

Issue

3

DOI

10.1080/19393555.2020.1722867

First Page

103

Last Page

117

Abstract

Spam can be defined as unsolicited bulk e-mail. In an effort to evade text-based filters, spammers sometimes embed spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply convolutional neural networks (CNN) to this problem, we compare the results obtained using CNNs to other machine learning techniques, and we compare our results to previous related work. We consider both real-world image spam and challenging image spam-like datasets. Our results improve on previous work by employing CNNs based on a novel feature set consisting of a combination of the raw image and Canny edges.

Keywords

Convolutional neural network, image spam, multilayer perceptron, support vector machine

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