Image Spam Filtering Using Machine Learning Techniques
Abhishek Rungta1, Bhawna Arya2, G. Usha3
1Abhishek Rungta, Department of Software Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Bhawna Arya, Department of Software Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Dr. G. Usha, Associate Professor, Department of Software Engineering, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 03 July 2019 | Revised Manuscript received on 13 August 2019 | Manuscript Published on 27 August 2019 | PP: 186-190 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B10350782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1035.0782S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Artificial Neural Networks, Data Augmentation, Optical Character Recognition, Support Vector Machines.
Scope of the Article: Machine Learning