An Efficient Feature Extraction Method for Classification of Image Spam Using Artificial Neural Networks

The widespread use of the internet has lead to enormous benefits to the internet users. However the use of one type of these facilities, the email system, has been highly damaged by the uncontrolled flooding of unwanted commercial messages, so called spam. Image spamming is a new kind of method of email spamming in which the text is embedded in image or picture files. Identifying and preventing spam is one of the top challenges in the internet world. The back propagation neural network is an effective classification method for solving feature extraction problems. In this paper we present an experimental system for the classification of image spam by considering single image feature, color histogram. The experimental result shows the performance of the proposed system and it achieves best results with minimum false positive.