Detecting Computer Generated Images for Image Spam Filtering

Author(s):  
Zubaidah Muataz Hazza ◽  
Normaziah Abdul Aziz
Keyword(s):  
Author(s):  
Hailing Huang ◽  
Weiqiang Guo ◽  
Yu Zhang

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740030 ◽  
Author(s):  
Rui Chang

This research proposes a three-layer image-spam filtering system. The system filters the image spam by analyzing both the mail header and image. We elaborate the structure of the model and explicate carefully our idea of the design and many technologies related to the model. Experimental results show that this system has satisfactory filtering effect.


2013 ◽  
Vol 37 (4) ◽  
pp. 517-528 ◽  
Author(s):  
Tzong-Jye Liu ◽  
Cheng-Nan Wu ◽  
Chia-Lin Lee ◽  
Ching-Wen Chen
Keyword(s):  

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.


2015 ◽  
Vol 3 (4) ◽  
pp. 72-86
Author(s):  
So Yeon Kim ◽  
Kyung-Ah Sohn

Spam images in mobile phones have increasingly appeared these days. As the spam filtering systems become more sophisticated, spams are being more intelligent. Although detection of email-spams has been quite successful, there have not been effective solutions for detecting mobile phone spams yet, especially, spam images. In addition to the expensive image processing time, insufficient spam image data in mobile phones makes it challenging to train a general model. To address this issue, the authors propose a graph-based approach that utilizes graph structure in abundant e-mail spam dataset. The authors employ different clustering algorithms to find a subset of e-mail spam images similar to phone spam images. Furthermore, the performance behavior with respect to different image descriptors of Pyramid Histogram of Visual Words (PHOW) and RGB histogram is extensively investigated. The authors' results highlight that the proposed idea is fairly meaningful in increasing training data size, thus effectively improving image spam detection performance.


2015 ◽  
Vol 48 (10) ◽  
pp. 3227-3238 ◽  
Author(s):  
Jialie Shen ◽  
Robert H. Deng ◽  
Zhiyong Cheng ◽  
Liqiang Nie ◽  
Shuicheng Yan

Author(s):  
Li Xiao Mang ◽  
HaRim Jung ◽  
Hee Yong Youn ◽  
Ung-Mo Kim

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