Advanced Image-Based Spam Detection and Filtering TechniquesSecurity technologies have advanced at an accelerated pace in the past few decades. These advancements in cyber security have benefitted many organizations and companies interested in protecting their virtual assets. Advanced Image-Based Spam Detection and Filtering Techniques provides a detailed examination of the latest strategies and methods used to protect against virtual spam. Featuring comprehensive coverage across a range of related topics such as image filters, optical character recognition, fuzzy inference systems, and near-duplicate detection, this book is an ideal reference source for engineers, business managers, professionals, and researchers seeking innovative technologies to aid in spam recognition. |
What people are saying - Write a review
We haven't found any reviews in the usual places.
Contents
1 | |
Image Spam | 28 |
Image Spam | 58 |
Image Spam Filters Based on Optical Character Recognition OCR Techniques | 90 |
Near Duplicate DetectionBased Image Spam Filters | 109 |
Visual FeatureBased Image Spam Filters | 123 |
Other editions - View all
Advanced Image-Based Spam Detection and Filtering Techniques Sunita Vikrant Dhavale No preview available - 2017 |
Common terms and phrases
algorithm analysis Available based spam detection CAPTCHA classifier color histogram Computer detection accuracy Dredze edge email address email spam embedded text Equation F-ISDS feature extraction feature vector frequency Function to Calculate Gabor filters Gaussian genuine images given ham and spam ham images hence IGI Global image based spam image features image spam detection Image Spam Filtering image-based spam Information input International Conference Internet keywords layer legitimate image level features linear Linear Regression MAAWG machine learning matrix metadata natural images noise obfuscating techniques OCR based OCR methods OCR techniques Optical Character Recognition output pixel processing proposed recipient related features sample spam image saturation shown in Figure spam and ham spam detection techniques spam images spam mails spam messages spammers strcmp supervised learning Support Vector Machine Technology template text area text spam total number users utilized visual features words Zhang