A Survey On Image Spam Detection Techniques

Khawandi, Shadi; Abdallah, Fahed; Ismail, Anis

doi:10.5121/csit.2019.90102

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…We tabulate the accuracy obtained for these systems and represent the performance of various sets of classifiers. As shown in Table 1, we present a summarization of classifier-type, performance and, dataset Using [28][29][30][31][32][33] papers. We observe that the performance of ANN is better than others.…”

Section: Resultsmentioning

confidence: 99%

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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%

“…Spam attackers deploy new approaches to transfer malicious content over the internet. Some of the classes of spam [3] are randomization, colouring, wild backgrounds, text only, and broken images [4]. Spam advertisements are a famous means of hype for sales marketers.…”

Section: Classification Techniquesmentioning

confidence: 99%

Image Spam Filtering using Machine Learning Techniques

2019

ijrte

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“…al. [4] share their concern over image spam detection since it has been serious issue over the years and numerous solutions have been provided by different vendors. This paper focuses on the process used for preventing spams while explaining the available solutions for handling spam and image based spam.…”

Section: Types Of Spamsmentioning

confidence: 99%

Logistic Regression Based Classification of Spam and Non-Spam Emails

Khanday¹,

Parveen²

2021

Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 Feb

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An email client receives emails from different websites, portals and domains, which can be an advertisement. Receiving a bulk amount of emails can cause serious damages like suspension of a particular email id. Mostly an email client gets exposed to the number of malicious receipts by registering an email account to a web portal, which in turn sends a bulk amount of emails. One of the solutions to escape from spam emails is to develop a decision based system which can classify the spam and non-spam emails. This can be achieved using different machine learning and deep learning and deep learning algorithms to classify the spam and non-spam emails by accessing the received emails of an email client. The machine learning approaches and mechanisms like SVM, naive Bayesian classifier, artificial neural networks and random forests can be of important help to determine spam emails. After classifying a spam email source a user can navigate, block and report the source of the spam email generator like spam-bots.

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“…al. paper [4] share their concern over image spam detection since it has been serious issue over the years and numerous solutions have been provided by different vendors. This paper focuses on the process used for preventing spams while explaining the available solutions for handling spam and image based spam.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Various Machine Learning and Deep Learning Algorithms used for Classification of Spam and Non-Spam Emails

Khanday¹

2019

IJRASET

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An email client receives emails from different websites, portals and domains, which can be an advertisement. Receiving a bulk amount of emails can cause serious damages like suspension of a particular email id.Mostly an email client gets exposed to the number of malicious receipts by registering an email account to a web portal, which in turn sends a bulk amount of emails. The email client wants to be decisive about differentiating the useful emails and spam emails. One of the solutions to escape from spam emails is to develop a decision based system which can classify the spam and non-spam emails. This survey gives an overview about different machine learning and deep learning algorithms to classify the spam and non-spam emails by accessing the received emails of an email client. The machine learning approaches and mechanisms like support vector machine, naive Bayesian classifier, artificial neural networks and logistic regression can be of important help to determine spam emails. These approaches use decision trees to run tests on a given sets of data (emails). After classifying a spam email source a user can navigate, block and report the source of the spam email generator. Most of the times the spam emails are generated by the autonomous sources which are called spam-bots.

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A Survey On Image Spam Detection Techniques

Cited by 6 publications

References 9 publications

Image Spam Filtering using Machine Learning Techniques

Image Spam Filtering using Machine Learning Techniques

Logistic Regression Based Classification of Spam and Non-Spam Emails

A Survey on Various Machine Learning and Deep Learning Algorithms used for Classification of Spam and Non-Spam Emails

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