An image spam detection method

Su, Chun-Yuan; Shen, Day-Fann; Lin, Guo-Shiang

doi:10.1109/icce-china.2017.7991000

Cited by 2 publications

(1 citation statement)

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“…Spam messages cause serious problems in our inboxes. Most email readers spend considerable amounts of time, on a regular basis, removing junk emails, while the increasing numbers of such emails take up storage space on servers and consume bandwidth on the networks [4]. Because of this, it is important to discriminate between legitimate and spam emails.…”

Section: Introductionmentioning

confidence: 99%

Image Spam Detection Using Machine Learning and Natural Language Processing

Yaseen¹,

Abbas²,

Sana³

2020

Journal of Southwest Jiaotong University

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Today, images are a part of communication between people. However, images are being used to share information by hiding and embedding messages within it, and images that are received through social media or emails can contain harmful content that users are not able to see and therefore not aware of. This paper presents a model for detecting spam on images. The model is a combination of optical character recognition, natural language processing, and the machine learning algorithm. Optical character recognition extracts the text from images, and natural language processing uses linguistics capabilities to detect and classify the language, to distinguish between normal text and slang language. The features for selected images are then extracted using the bag-of-words model, and the machine learning algorithm is run to detect any kind of spam that may be on it. Finally, the model can predict whether or not the image contains any harmful content. The results show that the proposed method using a combination of the machine learning algorithm, optical character recognition, and natural language processing provides high detection accuracy compared to using machine learning alone.

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Section: Introductionmentioning

confidence: 99%