A study on e-mail image spam filtering techniques

Dhanaraj, S.; Karthikeyani, V.

doi:10.1109/icprime.2013.6496446

Cited by 30 publications

(23 citation statements)

References 15 publications

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“…The papers [19][20][21][22][23] present the various approaches which are applied to classify an image as useful or useless. In [24], the author uses low-level features and the OCR technique using SVM classifier to obtain 95 per cent accuracy.…”

Section: Resultsmentioning

confidence: 99%

Image Spam Filtering using Machine Learning Techniques

2019

ijrte

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Unsolicited visual data is undesirable in any form. The art of hiding malicious content in images and adding them as attachments to electronic mails has become a popular nuisance. In recent years, attackers have developed various new techniques to evade traditional spam classification systems. Text-based spam classification has been in focus for a long time and, researchers have successfully created a prodigal system for identifying spam text in electronic mails using Optical Character Recognition technology. In the last decade, extensive work has been performed to tackle image spam but with unsatisfactory results. Various algorithms and data augmentation techniques are used today to develop an optimal model for image spam recognition. Many of these proposed systems come close to the ideal system but do not provide 100 percent accuracy. This paper highlights the role of three popular techniques in image spam filtering. We discuss the importance and application of Optical Character Recognition, Support Vector Machines and, Artificial Neural Networks in unsolicited visual data filtering. This paper sheds light on the algorithms of these techniques. We provide a comparison of their accuracy, which helps us draw useful insights for developing a robust unsolicited visual data classification system. This paper aims to bring clarity regarding the feasibility of using these techniques to develop an unsolicited visual data filtering system. This paper records that the most favourable results are obtained using Artificial Neural Networks.

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Section: Resultsmentioning

confidence: 99%

Image Spam Filtering using Machine Learning Techniques

2019

ijrte

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“…As previously noted, SVMs have been applied with success to the text-based spam detection problem (Dhanaraj and Karthikeyani, 2013). In this section, we first consider SVMs for image spam detection, and we use SVMs for feature reduction.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…These techniques extract the actual content from within each spam email and classifiers are built based on keywords, headers, payload, etc. Machine learning techniques have been extensively used for such classifiers (Dhanaraj and Karthikeyani, 2013).…”

Section: Spam Detection Techniquesmentioning

confidence: 99%

Support Vector Machines for Image Spam Analysis

Chavda

Potika

Troia

et al. 2018

Proceedings of the 15th International Joint Conference on E-Business and Telecommunications

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Email is one of the most common forms of digital communication. Spam is unsolicited bulk email, while image spam consists of spam text embedded inside an image. Image spam is used as a means to evade textbased spam filters, and hence image spam poses a threat to email-based communication. In this research, we analyze image spam detection using support vector machines (SVMs), which we train on a wide variety of image features. We use a linear SVM to quantify the relative importance of the features under consideration. We also develop and analyze a realistic "challenge" dataset that illustrates the limitations of current image spam detection techniques. 2 BACKGROUND In this section, we briefly consider spam detection techniques. Then we discuss research that is most clo

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“…Email usage has been increasing along with the growth of such unwanted side effects, and spam and fraudulent email messages are now major concerns for email users. Dhanaraj & Karthikeyani [5] clearly state "Spam was once just an annoyance, but it has now become the tactic of choice for online deception, fraud, and abuse. The freedom of communication is being misused and has become a threat to email communication society".…”

Section: Spam and Phishingmentioning

confidence: 99%