An Observation and Experimental Evaluation of Image Spam Detection
Mallikka Rajalingam1, M. Balamurugan2
1Mallikka Rajalingam, Department of Computer Science and Engineering, Bharathidasan University, Trichy, India.
2M. Balamurugan, Department of Computer Science and Engineering, Bharathidasan University, Trichy, India.
Manuscript received on 03 August 2019. | Revised Manuscript received on 09 August 2019. | Manuscript published on 30 September 2019. | PP: 5892-5896 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4733098319/19©BEIESP | DOI: 10.35940/ijrte.C4733.098319
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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: In belonging to other supports duel beside researchers of image spam detections, unsolicited mail have newly developed the image based spam dodge to construct the investigation of e-mails’ content of text unsuccessful. To avoid signature based recognition, it involves in implanting the unsolicited text or message into an appendage image, which is frequently arbitrarily customized. Identifying image based spam emails tries out to be an motivating illustration of the problem text embedded in images were subjected to noise such as background pattern, color, font variations and imperfections in a font size so as to eliminate the chances of being identified as unsolicited e-mail by classification techniques. In this research paper we spring a exhaustive review and categorization of machine learning and classification systems suggested so far in contradiction of image based spam email, and make an empirical investigation and correlation of few of them on real, widely accessible data sets.
Index Terms: Image Spam Detection, Recognition, Segmentation, and Support Vector Machine.
Scope of the Article: Image Security