Detecting Computer Generated Images for Image Spam Filtering

2012 ◽  
Author(s):  
Zubaidah Muataz Hazza ◽  
Normaziah Abdul Aziz
Keyword(s):  
Spam Filtering ◽  
Image Spam

A Novel Method for Image Spam Filtering

2008 ◽  
Author(s):  
Hailing Huang ◽  
Weiqiang Guo ◽  
Yu Zhang
Keyword(s):  
Spam Filtering ◽  
Novel Method ◽  
Image Spam

Application of multi-layer algorithm on image spam filtering

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740030 ◽  
Author(s):  
Rui Chang
Keyword(s):  
Experimental Results ◽  
Spam Filtering ◽  
Layer Image ◽  
Image Spam ◽  
Filtering Effect

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.

A self-adaptable image spam filtering system

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

scholarly journals Image Spam Filtering using Machine Learning Techniques

2019 ◽  
Vol 8 (2S4) ◽  
pp. 186-190
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Spam Filtering ◽  
Data Filtering ◽  
Visual Data ◽  
Optical Character ◽  
Image Spam ◽  
Artificial Neural

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.

Graph-Based Spam Image Detection for Mobile Phone Spam Image Filtering

2015 ◽  
Vol 3 (4) ◽  
pp. 72-86
Author(s):  
So Yeon Kim ◽  
Kyung-Ah Sohn
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Clustering Algorithms ◽  
Image Data ◽  
Training Data ◽  
Spam Filtering ◽  
Visual Words ◽  
Image Descriptors ◽  
Image Spam ◽  
E Mail

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.

scholarly journals On robust image spam filtering via comprehensive visual modeling

2015 ◽  
Vol 48 (10) ◽  
pp. 3227-3238 ◽  
Author(s):  
Jialie Shen ◽  
Robert H. Deng ◽  
Zhiyong Cheng ◽  
Liqiang Nie ◽  
Shuicheng Yan
Keyword(s):  
Spam Filtering ◽  
Visual Modeling ◽  
Image Spam ◽  
Robust Image

scholarly journals An Incremental Learning Based Framework for Image Spam Filtering

2014 ◽  
Vol 4 (1) ◽  
pp. 25-40
Author(s):  
Li Xiao Mang ◽  
HaRim Jung ◽  
Hee Yong Youn ◽  
Ung-Mo Kim
Keyword(s):  
Incremental Learning ◽  
Spam Filtering ◽  
Image Spam
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