An Intelligent System for Detecting Image Spam based on Exploration of Text Information Embedded into Images
Mallikka Rajalingam1, M. Balamurugan2
1Mallikka Rajalingam*, Department of Computer Science and Engineering, Bharathidasan University, Trichy, India.
1Dr. M. Balamurugan, Department of Computer Science and Engineering, Bharathidasan University, Trichy, India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 20, 2019. | Manuscript published on January 10, 2020. | PP: 2403-2408 | Volume-9 Issue-3, January 2020. | Retrieval Number: C9108019320/2020©BEIESP | DOI: 10.35940/ijitee.C9108.019320
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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: Spam mails act as a immense hazard to communication security as it leads to phishing or virus attacks that could harm the user accounts and the organizations by exposing confidential information.With these inferences, the present research attempted to accurately detect image spam emails which have always been a topic of great research in data security. Detection of image spam mails is divided into two separate components- character segmentation and character recognition. While the former segments individual characters, the latter overcomes the issue of blocked texts and not well surveyed. The final phase of the work is a complete image spam detection system with the two proposed works built to detect spam messages. Various techniques have been proposed to tackle this setback and the principle of this paper is to evaluate and review several algorithms, talk about benchmark statistics or data, show appraisal of performance followed by potential direction for future work.
Keywords: Feature Extraction, Character Segmentation, Character Recognition, Image Spam Detection, and Multi-SVM.
Scope of the Article: Evolutionary Computing and Intelligent Systems