scholarly journals A Survey On Image Spam Detection Techniques

Author(s):  
Shadi Khawandi ◽  
Firas Abdallah ◽  
Anis Ismail

In order to understand the never-ending fights between developers of anti-spam detection techniques and the spammers; it is important to have an insight of the history of spam mails. On May 3, 1978, Gary Thuerk, a marketing manager at Digital Equipment Corporation sent his first mass email to more than 400 customers over the Arpanet in order to promote and sell Digital's new T-Series of VAX systems (Streitfeld, 2003). In this regard, he said, “It's too much work to send everyone an e-mail. So we'll send one e-mail to everyone”. He said with pride, “I was the pioneer. I saw a new way of doing things.” As every coin has two sides, any technology too can be utilized for good and bad intention. At that time, Gary Thuerk would have never dreamt of this method of sending mails to emerge as an area of research in future. Gary Thuerk ended up getting crowned as the father of spam mails instead of the father of e-marketing. In the present scenario, the internet receives 2.5 billion pieces of spam a day by spiritual followers of Thuerk.


2017 ◽  
Vol 77 (11) ◽  
pp. 13249-13278 ◽  
Author(s):  
Amiza Amir ◽  
Bala Srinivasan ◽  
Asad I. Khan

Spam features represent the unique and special characteristics associated with spam, which are further used to differentiate them from other genuine messages. Each message m is processed by a feature extraction module to represent m in terms of n dimensional feature vector x = (x1, x2, …, xn) containing n features. This feature vector consists of many such features extracted from spam. In case of text based spam filters, a feature can be a word and a feature vector may be composed of various words extracted from spam. Each spam is associated with one feature vector. Based on the characteristics discussed in previous chapter, we will try to extract different features capturing those unique characteristics from image spam, in order to build the robust spam detection algorithms further. These features are broadly classified into high level metadata features, low level image features like color features, grayscale features, texture related features and embedded text related features.


2019 ◽  
Vol 9 (5) ◽  
pp. 987 ◽  
Author(s):  
Naveed Hussain ◽  
Hamid Turab Mirza ◽  
Ghulam Rasool ◽  
Ibrar Hussain ◽  
Mohammad Kaleem

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.


Author(s):  
Vyas Krishna Maheshchandra ◽  
Prof. Ankit P. Vaishnav ◽  

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